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1
Rocchini, Duccio. "Ecological Remote Sensing: A Challenging Section on Ecological Theory and Remote Sensing." Remote Sensing 13, no. 5 (February 25, 2021): 848. http://dx.doi.org/10.3390/rs13050848.
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Han, Yanling, Cong Wei, Ruyan Zhou, Zhonghua Hong, Yun Zhang, and Shuhu Yang. "Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification." Mathematical Problems in Engineering 2020 (April 7, 2020): 1–15. http://dx.doi.org/10.1155/2020/8065396.
Повний текст джерелаАнотація:
Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice classification accuracy. For this issue, this paper proposes a new remote sensing sea ice image classification method based on squeeze-and-excitation (SE) network, convolutional neural network (CNN), and support vector machines (SVMs). The proposed method designs 3D-CNN deep network so as to fully exploit the spatial-spectrum features of remote sensing sea ice images and integrates SE-Block into 3D-CNN in-depth network in order to distinguish the contributions of different spectra to sea ice classification. According to the different contributions of spectral features, the weight of each spectral feature is optimized by fusing SE-Block in order to further enhance the sample quality. Finally, information-rich and representative samples are chosen by combining the idea of active learning and input into SVM classifier, and this achieves superior classification accuracy of remote sensing sea ice images with small samples. In order to verify the effectiveness of the proposed method, we conducted experiments on three different data from Baffin Bay, Bohai Bay, and Liaodong Bay. The experimental results show that compared with other classical classification methods, the proposed method comprehensively considers the correlation among spectral features and the small samples problems and deeply excavates the spatial-spectrum characteristics of sea ice and achieves better classification performance, which can be effectively applied to remote sensing sea ice image classification.
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Wei, Lifei, Ming Yu, Yajing Liang, Ziran Yuan, Can Huang, Rong Li, and Yiwei Yu. "Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery." Remote Sensing 11, no. 17 (August 27, 2019): 2011. http://dx.doi.org/10.3390/rs11172011.
Повний текст джерелаАнотація:
The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial resolution has become the ideal data source for the precise classification of crops. For precise classification of crops with a wide variety of classes and varied spectra, the traditional spectral-based classification method has difficulty in mining large-scale spatial information and maintaining the detailed features of the classes. Therefore, a precise crop classification method using spectral-spatial-location fusion based on conditional random fields (SSLF-CRF) for UAV-borne hyperspectral remote sensing imagery is proposed in this paper. The proposed method integrates the spectral information, the spatial context, the spatial features, and the spatial location information in the conditional random field model by the probabilistic potentials, providing complementary information for the crop discrimination from different perspectives. The experimental results obtained with two UAV-borne high spatial resolution hyperspectral images confirm that the proposed method can solve the problems of large-scale spatial information modeling and spectral variability, improving the classification accuracy for each crop type. This method has important significance for the precise classification of crops in hyperspectral remote sensing imagery.
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Duan, Meimei, and Lijuan Duan. "High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing." Scientific Programming 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/4356957.
Повний текст джерелаАнотація:
Existing remote sensing data classification methods cannot achieve the sharing of remote sensing image spectrum, leading to poor fusion and classification of remote sensing data. Therefore, a high spatial resolution remote sensing data classification method based on spectrum sharing is proposed. A page frame recovery algorithm (PFRA) is introduced to allocate the wireless spectrum resources in low-frequency band, and a dynamic spectrum sharing mechanism is designed between the primary and secondary users of remote sensing images. Based on this, D-S evidence theory is used to fuse high spatial resolution remote sensing data and correct the pixel brightness of the fused multispectral image. The initial data are normalized, the feature of spectral image is extracted, the convolution neural network classification model is constructed, and the remote sensing image is segmented. Experimental results show that the proposed method takes shorter time and has higher accuracy for high spatial resolution image segmentation. High spatial resolution remote sensing data classification is more efficient, and the accuracy of data classification and remote sensing image fusion are more ideal.
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Peng, Mingyuan, Lifu Zhang, Xuejian Sun, Yi Cen, and Xiaoyang Zhao. "A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset." Remote Sensing 12, no. 23 (November 27, 2020): 3888. http://dx.doi.org/10.3390/rs12233888.
Повний текст джерелаАнотація:
With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemporal fusion method (STF3DCNN) using a spatial-temporal-spectral dataset. This method is able to fuse low-spatial high-temporal resolution data (HTLS) and high-spatial low-temporal resolution data (HSLT) in a four-dimensional spatial-temporal-spectral dataset with increasing efficiency, while simultaneously ensuring accuracy. The method was tested using three datasets, and discussions of the network parameters were conducted. In addition, this method was compared with commonly used spatiotemporal fusion methods to verify our conclusion.
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Imanian, A., M. H. Tangestani, and A. Asadi. "INVESTIGATION OF SPECTRAL CHARACTERISTICS OF CARBONATE ROCKS – A CASE STUDY ON POSHT MOLEH MOUNT IN IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 553–57. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-553-2019.
Повний текст джерелаАнотація:
Abstract. Recent developments in the image processing approaches and the availability of multi and/or hyper spectral remote sensing data with high spectral, spatial and temporal resolutions have made remote sensing technique of great interest in investigations of geological sciences. One of the biggest advantage of the application of remote sensing in geology is recognizing the type of unknown rocks and minerals. In this study, an investigation on spectral features of carbonate rocks (i.e. calcite, dolomite, and dolomitized calcite) were done in terms of main absorptions, the reasons of those absorptions and comparison of these absorption with Johns Hopkins University (JHU) spectral library and laboratory spectra of Analytical Spectral Devices (ASD) instrument. For this purpose, we used the VNIR and SWIR bands of ASTER and OLI datasets. Finally, we applied the Spectral Analyst Algorithm in order to comparison between the obtained spectra from ASTER dataset and carbonate spectra of JHU spectral library.
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Xu, Qingsong, Xin Yuan, Chaojun Ouyang, and Yue Zeng. "Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images." Remote Sensing 12, no. 21 (October 24, 2020): 3501. http://dx.doi.org/10.3390/rs12213501.
Повний текст джерелаАнотація:
Unlike conventional natural (RGB) images, the inherent large scale and complex structures of remote sensing images pose major challenges such as spatial object distribution diversity and spectral information extraction when existing models are directly applied for image classification. In this study, we develop an attention-based pyramid network for segmentation and classification of remote sensing datasets. Attention mechanisms are used to develop the following modules: (i) a novel and robust attention-based multi-scale fusion method effectively fuses useful spatial or spectral information at different and same scales; (ii) a region pyramid attention mechanism using region-based attention addresses the target geometric size diversity in large-scale remote sensing images; and (iii) cross-scale attention in our adaptive atrous spatial pyramid pooling network adapts to varied contents in a feature-embedded space. Different forms of feature fusion pyramid frameworks are established by combining these attention-based modules. First, a novel segmentation framework, called the heavy-weight spatial feature fusion pyramid network (FFPNet), is proposed to address the spatial problem of high-resolution remote sensing images. Second, an end-to-end spatial-spectral FFPNet is presented for classifying hyperspectral images. Experiments conducted on ISPRS Vaihingen and ISPRS Potsdam high-resolution datasets demonstrate the competitive segmentation accuracy achieved by the proposed heavy-weight spatial FFPNet. Furthermore, experiments on the Indian Pines and the University of Pavia hyperspectral datasets indicate that the proposed spatial-spectral FFPNet outperforms the current state-of-the-art methods in hyperspectral image classification.
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NanLan, Wang, and Zeng Xiaoyong. "Hyperspectral Data Classification Algorithm considering Spatial Texture Features." Mobile Information Systems 2022 (March 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/9915809.
Повний текст джерелаАнотація:
As a cutting-edge technology, hyperspectral remote sensing has been widely applied in many fields, including agricultural production, mineral identification, target detection, disaster warning, military reconnaissance, and urban planning. The collected hyperspectral data have high spectral resolution and spatial resolution and are characterized by a large amount of information, redundancy, and high dimension. At the same time, there is a strong correlation between the bands. Therefore, hyperspectral data not only provides rich information but also brings great challenges for subsequent processing. Hyperspectral image classification is a hot issue in remote sensing information processing. Traditional hyperspectral remote sensing image classification methods only use the spectral features of the image without considering the spatial features of each pixel in the hyperspectral remote sensing image. In this paper, a hyperspectral image classification method is proposed not only considering spectral features but also considering texture features. This method jointly considers both these features. Firstly, six texture features contributing a lot to each pixel of hyperspectral remote sensing image are extracted by using a gray level cooccurrence matrix, and then, the spectral features of each pixel in neighbor are combined to form the texture-spectral features. Finally, the classification experiment of the Indian Pines and Pavia University scene is carried out based on a support vector machine and extreme random tree algorithm, and the obtained results show that the proposed method achieves higher classification performance than the traditional method.
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Zhao, Rui, and Shihong Du. "Spectral-Spatial Residual Network for Fusing Hyperspectral and Panchromatic Remote Sensing Images." Remote Sensing 14, no. 3 (February 8, 2022): 800. http://dx.doi.org/10.3390/rs14030800.
Повний текст джерелаАнотація:
Fusing hyperspectral and panchromatic remote sensing images can obtain the images with high resolution in both spectral and spatial domains. In addition, it can complement the deficiency of high-resolution hyperspectral and panchromatic remote sensing images. In this paper, a spectral–spatial residual network (SSRN) model is established for the intelligent fusion of hyperspectral and panchromatic remote sensing images. Firstly, the spectral–spatial deep feature branches are built to extract the representative spectral and spatial deep features, respectively. Secondly, an enhanced multi-scale residual network is established for the spatial deep feature branch. In addition, an enhanced residual network is established for the spectral deep feature branch This operation is adopted to enhance the spectral and spatial deep features. Finally, this method establishes the spectral–spatial deep feature simultaneity to circumvent the independence of spectral and spatial deep features. The proposed model was evaluated on three groups of real-world hyperspectral and panchromatic image datasets which are collected with a ZY-1E sensor and are located at Baiyangdian, Chaohu and Dianchi, respectively. The experimental results and quality evaluation values, including RMSE, SAM, SCC, spectral curve comparison, PSNR, SSIM ERGAS and Q metric, confirm the superior performance of the proposed model compared with the state-of-the-art methods, including AWLP, CNMF, GIHS, MTF_GLP, HPF and SFIM methods.
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Shi, Xue, Yu Wang, Yu Li, and Shiqing Dou. "Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing." Remote Sensing 15, no. 3 (February 1, 2023): 828. http://dx.doi.org/10.3390/rs15030828.
Повний текст джерелаАнотація:
Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student’s-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Considering the complex distribution of spectral intensities, the proposed algorithm constructs the HSMM to accurately build the statistical model of the image, making more reasonable use of the spectral information and improving segmentation accuracy. The component weight is defined by the attribute probability of neighborhood pixels to overcome the influence of image noise and make a simple and easy-to-implement structure. To avoid the effects of artificially setting the smoothing coefficient, the gradient optimization method is used to solve the model parameters, and the smoothing coefficient is optimized through iterations. The experimental results suggest that the proposed HSMM can accurately model asymmetric, heavy-tailed, and bimodal distributions. Compared with traditional segmentation algorithms, the proposed algorithm can effectively overcome noise and generate more accurate segmentation results for high-resolution remote sensing images.
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Zhou, Xiao Hu. "Geometric Distortion Correction of Geothermal Field Hyperspectral Remote Sensing Images in Lintong, Shanxi." Advanced Materials Research 383-390 (November 2011): 4158–62. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4158.
Повний текст джерелаАнотація:
The accurate delineation of geothermal resources with remote sensing technology is one hot topic in recent years. Remote sensing images used in previous studies, are mostly space multi-spectral remote sensing images, spectral resolution and spatial resolution are relatively low, difficult to accurately delineate the geothermal anomaly. Considering those research at home and abroad, using Hyperspectral resolution remote sensing images, choosing Lintong area for the study area. Because of hyperspectral remote sensing images exist in the acquisition process more obvious geometric distortion, during data processing, information extraction, geothermal anomaly delineated and so the information is needed before the image is first geometrically corrected in order to obtain high precision and high-quality remote sensing images. This research in this regard a useful exploration and obtained ideal result.
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Ge, Chuting, Haiyong Ding, Inigo Molina, Yongjian He, and Daifeng Peng. "Object-Oriented Change Detection Method Based on Spectral–Spatial–Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images." Remote Sensing 14, no. 14 (July 8, 2022): 3297. http://dx.doi.org/10.3390/rs14143297.
Повний текст джерелаАнотація:
Spectral features in remote sensing images are extensively utilized to detect land cover changes. However, detection noise appearing in the changing maps due to the abundant spatial details in the high-resolution images makes it difficult to acquire an accurate interpretation result. In this paper, an object-oriented change detection approach is proposed which integrates spectral–spatial–saliency change information and fuzzy integral decision fusion for high-resolution remote sensing images with the purpose of eliminating the impact of detection noise. First, to reduce the influence of feature uncertainty, spectral feature change is generated by three independent methods, and spatial change information is obtained by spatial feature set construction and the optimal feature selection strategy. Secondly, the saliency change map of bi-temporal images is obtained with the co-saliency detection method to complement the insufficiency of image features. Then, the image objects are acquired by multi-scale segmentation based on the staking images. Finally, different pixel-level image change information and the segmentation result are fused using the fuzzy integral decision theory to determine the object change probability. Three high-resolution remote sensing image datasets and three comparative experiments were carried out to evaluate the performance of the proposed algorithm. Spectral–spatial–saliency change information was found to play a major role in the change detection of high-resolution remote sensing images, and the fuzzy integral decision strategy was found to effectively obtain reliable changed objects to improve the accuracy and robustness of change detection.
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Harlander, John M., Fred L. Roesler, Christoph R. Englert, Joel G. Cardon, and Jeff Wimperis. "Spatial Heterodyne Spectroscopy For High Spectral Resolution Space-Based Remote Sensing." Optics and Photonics News 15, no. 1 (January 1, 2004): 46. http://dx.doi.org/10.1364/opn.15.1.000046.
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Zhang, Shuang, Yifei Han, Hua Wang, and Daishuang Hou. "Gram-Schmidt Remote Sensing Image Fusion Algorithm Based on Matrix Elementary Transformation." Journal of Physics: Conference Series 2410, no. 1 (December 1, 2022): 012013. http://dx.doi.org/10.1088/1742-6596/2410/1/012013.
Повний текст джерелаАнотація:
Abstract In the application of the Internet of vehicles, it is difficult for a single remote sensing image to have accurate spatial information and spectral information at the same time, so engineers must use image fusion to improve the utilization of remote sensing image information. Aiming at the shortcomings of low spectral resolution and relatively complex programming of traditional fusion algorithms, this paper proposes a Gram-Schmidt remote sensing image fusion algorithm based on matrix elementary transformation, which aims to improve the spatial resolution of multispectral images by using panchromatic images with high spatial resolution. The orthogonalization process of the algorithm in this paper uses the elementary matrix transformation, which is simple and easy to perform. The experimental results show that it can effectively improve the spatial resolution of the original image and has good spectral retention.
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Marang, Ian J., Patrick Filippi, Tim B. Weaver, Bradley J. Evans, Brett M. Whelan, Thomas F. A. Bishop, Mohammed O. F. Murad, Dhahi Al-Shammari, and Guy Roth. "Machine Learning Optimised Hyperspectral Remote Sensing Retrieves Cotton Nitrogen Status." Remote Sensing 13, no. 8 (April 7, 2021): 1428. http://dx.doi.org/10.3390/rs13081428.
Повний текст джерелаАнотація:
Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a mature cotton crop, which had high spatial (~5.2 cm) and spectral (5 nm) resolution over the spectral range 475–925 nm that allowed discrimination of individual crop rows and field features as well as a continuous spectral range for calculating derivative spectra. The nominal reflectance and its derivatives clearly highlighted the different treatment blocks and were strongly related to N concentration in leaf and petiole samples, both in traditional vegetation indices (e.g., Vogelman 1, R2 = 0.8) and novel combinations of spectra (R2 = 0.85). The key hyperspectral bands identified were at the red-edge inflection point (695–715 nm). Satellite multispectral was compared against the UAV hyperspectral remote sensing’s performance by testing the ability of Sentinel MSI to predict N concentration using the bands in VIS-NIR spectral region. The Sentinel 2A Green band (B3; mid-point 559.8 nm) explained the same amount of variation in N as the hyperspectral data and more than the Sentinel Red Edge Point 1 (B5; mid-point 704.9 nm) with the lower 10 m resolution Green band reporting an R2 = 0.85, compared with the R2 = 0.78 of downscaled Sentinel Red Edge Point 1 at 5 m. The remaining Sentinel bands explained much lower variation (maximum was NIR at R2 = 0.48). Investigation of the red edge peak region in the first derivative showed strong promise with RIDAmid (R2 = 0.81) being the best index. The machine learning approach narrowed the range of bands required to investigate plant condition over this trial site, greatly improved processing time and reduced processing complexity. While Sentinel performed well in this comparison and would be useful in a broadacre crop production context, the impact of pixel boundaries relative to a region of interest and coarse spatial and temporal resolution impacts its utility in a research capacity.
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Cui, B., W. J. Huang, H. C. Ye, Q. X. Chen, Z. C. Li, and H. Y. Jiang. "Optimal spatial resolution of remote-sensing imagery for monitoring cantaloupe greenhouses." IOP Conference Series: Earth and Environmental Science 1004, no. 1 (March 1, 2022): 012020. http://dx.doi.org/10.1088/1755-1315/1004/1/012020.
Повний текст джерелаАнотація:
Abstract Plastic greenhouses are vital agricultural facilities to protect cash crops from disease and insects, especially in the Hainan region of China, which has high temperature and high humidity. Remote-sensing technology is an efficient means to quickly determine the spatial distribution of plastic greenhouses on the regional scale. With the rapid development of remote-sensing technology, and especially the increasing types of high-spatial-resolution remote-sensing imagery, many studies have obtained good results by using remote-sensing technology to monitor plastic greenhouses. However, the best spatial resolution of images for monitoring plastic greenhouses has yet to be studied. To address this issue, we use cantaloupe greenhouses as the research object and GF-2 images with 1m spatial resolution as data source. We then use the re-sampling method to generate images from these data with spatial resolutions of 0.5, 2, 3, and 5 m. The details of the spatial distribution (texture features and shape features) and the spectral features of the plastic greenhouses were then extracted from images of varying spatial resolution, and a remote-sensing monitoring method for cantaloupe greenhouses was constructed based on the object-oriented random forest algorithm, which combines spectral, texture and shape features, and the monitoring results are compared. The results show that the use of 2 m spatial resolution provides the highest monitoring accuracy of cantaloupe greenhouses (overall accuracy = 94.85% and KIA = 0.92). This study thus provides a theoretical basis for remote-sensing monitoring of greenhouse cantaloupes that satisfies the current demands of production accuracy.
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Guan, X., W. Qi, J. He, Q. Wen, T. Chen, and Z. Wang. "PURIFICATION OF TRAINING SAMPLES BASED ON SPECTRAL FEATURE AND SUPERPIXEL SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 425–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-425-2018.
Повний текст джерелаАнотація:
Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manually interpreted products may not match the very high resolution (VHR) image properly because of different dates or spatial resolution of both data, thus, hindering suitability of manually interpreted products in training classification models, or small coverage area of these manually interpreted products. We also face similar problems in our laboratory in 21st Century Aerospace Technology Co. Ltd (short for 21AT). In this work, we propose a method to purify the interpreted product to match newly available VHRI data and provide the best training data for supervised image classifiers in VHR image classification. And results indicate that our proposed method can efficiently purify the input data for future machine learning use.
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Sun, Y., Y. Lin, X. Hu, S. Zhao, S. Liu, Q. Tong, D. Helder, and L. Yan. "THE STUDY OF SPECTRUM RECONSTRUCTION BASED ON FUZZY SET FULL CONSTRAINT AND MULTIENDMEMBER DECOMPOSITION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 551–55. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-551-2017.
Повний текст джерелаАнотація:
Hyperspectral imaging system can obtain spectral and spatial information simultaneously with bandwidth to the level of 10 nm or even less. Therefore, hyperspectral remote sensing has the ability to detect some kinds of objects which can not be detected in wide-band remote sensing, making it becoming one of the hottest spots in remote sensing. In this study, under conditions with a fuzzy set of full constraints, Normalized Multi-Endmember Decomposition Method (NMEDM) for vegetation, water, and soil was proposed to reconstruct hyperspectral data using a large number of high-quality multispectral data and auxiliary spectral library data. This study considered spatial and temporal variation and decreased the calculation time required to reconstruct the hyper-spectral data. The results of spectral reconstruction based on NMEDM showed that the reconstructed data has good qualities and certain applications, which makes it possible to carry out spectral features identification. This method also extends the application of depth and breadth of remote sensing data, helping to explore the law between multispectral and hyperspectral data.
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Kumar, Suresh, and Vijay Bhagat. "Remote Sensing Satellites for Land Applications: A Review." Remote Sensing of Land 2, no. 2 (July 4, 2019): 96–104. http://dx.doi.org/10.21523/gcj1.18020203.
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Satellite remote sensing offers a unique opportunity in deriving various components of land information by integrating with ground based observation. Currently several remote sensing satellites are providing multispectral, hyperspectral and microwave data to cater the need of various land applications. Several old age remote sensing satellites have been updated with new generation satellites offering high spatial, spectral and temporal resolution. Microwave remote sensing data is now available with high spatial resolution and providing land information in cloudy weather condition that strengthening availability of remote sensing data in all days. Spatial resolution has significantly improved over the decades and temporal resolution has improved from months to daily. Indian Remote Sensing programs are providing state of the art satellite data in optical and microwave wavelength regions to meet large land applications in the country. Today several remote sensing data is available as open data sources. Upcoming satellite remote sensing data will help in precise characterization and quantification of land resources to support in sustainable land development planning to meet future challenges.
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Shao, Donghang, Wenbo Xu, Hongyi Li, Jian Wang, and Xiaohua Hao. "Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations." Remote Sensing 12, no. 18 (September 22, 2020): 3101. http://dx.doi.org/10.3390/rs12183101.
Повний текст джерелаАнотація:
Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.
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Wu, Yuanyuan, Siling Feng, Cong Lin, Haijie Zhou, and Mengxing Huang. "A Three Stages Detail Injection Network for Remote Sensing Images Pansharpening." Remote Sensing 14, no. 5 (February 22, 2022): 1077. http://dx.doi.org/10.3390/rs14051077.
Повний текст джерелаАнотація:
Multispectral (MS) pansharpening is crucial to improve the spatial resolution of MS images. MS pansharpening has the potential to provide images with high spatial and spectral resolutions. Pansharpening technique based on deep learning is a topical issue to deal with the distortion of spatio-spectral information. To improve the preservation of spatio-spectral information, we propose a novel three-stage detail injection pansharpening network (TDPNet) for remote sensing images. First, we put forward a dual-branch multiscale feature extraction block, which extracts four scale details of panchromatic (PAN) images and the difference between duplicated PAN and MS images. Next, cascade cross-scale fusion (CCSF) employs fine-scale fusion information as prior knowledge for the coarse-scale fusion to compensate for the lost information during downsampling and retain high-frequency details. CCSF combines the fine-scale and coarse-scale fusion based on residual learning and prior information of four scales. Last, we design a multiscale detail compensation mechanism and a multiscale skip connection block to reconstruct injecting details, which strengthen spatial details and reduce parameters. Abundant experiments implemented on three satellite data sets at degraded and full resolutions confirm that TDPNet trades off the spectral information and spatial details and improves the fidelity of sharper MS images. Both the quantitative and subjective evaluation results indicate that TDPNet outperforms the compared state-of-the-art approaches in generating MS images with high spatial resolution.
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Bai, Shi, and Jie Zhao. "A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods." Remote Sensing 15, no. 4 (February 8, 2023): 930. http://dx.doi.org/10.3390/rs15040930.
Повний текст джерелаАнотація:
Geochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial accuracy of geochemical data. Geochemical sampling is sometimes difficult to conduct because of harsh natural and geographic conditions (e.g., mountainous areas with high altitude and complex terrain), meaning that only medium/low-precision survey data could be obtained, which may not be adequate for regional geochemical mapping and exploration. Modern techniques such as remote sensing could be used to address this issue. In recent decades, the development of remote sensing technology has provided a huge amount of earth observation data with high spatial, temporal and spectral resolutions. The advantage of rapid acquisition of spatial and spectral information of large areas has promoted the broad use of remote sensing data in geoscientific research. Remote sensing data can help to differentiate various ground features by recording the electromagnetic response of the surface to solar radiation. Many problems that occur during the process of fusing remote sensing and geochemical data have been reported, such as the feasibility of existing fusion methods and low fusion accuracies that are less useful in practice. In this paper, a new strategy for integrating geochemical data and remote sensing data (referred to as ASTER data) is proposed; this strategy is achieved through linear regression as well as random forest and support vector regression algorithms. The results show that support vector regression can obtain better results for the available data sets and prove that the strategy currently proposed can effectively support the fusion of high-spatial-resolution remote sensing data (15 m) and low-spatial-resolution geochemical data (2000 m) in wide-range accurate geochemical applications (e.g., lithological identification and geochemical exploration).
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Pena, J. A., T. Yumin, H. Liu, B. Zhao, J. A. Garcia, and J. Pinto. "REMOTE SENSING DATA FUSION TO DETECT ILLICIT CROPS AND UNAUTHORIZED AIRSTRIPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1363–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1363-2018.
Повний текст джерелаАнотація:
Remote sensing data fusion has been playing a more and more important role in crop planting area monitoring, especially for crop area information acquisition. Multi-temporal data and multi-spectral time series are two major aspects for improving crop identification accuracy. Remote sensing fusion provides high quality multi-spectral and panchromatic images in terms of spectral and spatial information, respectively. In this paper, we take one step further and prove the application of remote sensing data fusion in detecting illicit crop through LSMM, GOBIA, and MCE analyzing of strategic information. This methodology emerges as a complementary and effective strategy to control and eradicate illicit crops.
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Tasdemir, Kadim, Yaser Moazzen, and Isa Yildirim. "An Approximate Spectral Clustering Ensemble for High Spatial Resolution Remote-Sensing Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 5 (May 2015): 1996–2004. http://dx.doi.org/10.1109/jstars.2015.2424292.
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Wang, Peng, Gong Zhang, Siyuan Hao, and Liguo Wang. "Improving Remote Sensing Image Super-Resolution Mapping Based on the Spatial Attraction Model by Utilizing the Pansharpening Technique." Remote Sensing 11, no. 3 (January 26, 2019): 247. http://dx.doi.org/10.3390/rs11030247.
Повний текст джерелаАнотація:
The spatial distribution information of remote sensing images can be derived by the super-resolution mapping (SRM) technique. Super-resolution mapping, based on the spatial attraction model (SRMSAM), has been an important SRM method, due to its simplicity and explicit physical meanings. However, the resolution of the original remote sensing image is coarse, and the existing SRMSAM cannot take full advantage of the spatial–spectral information from the original image. To utilize more spatial–spectral information, improving remote sensing image super-resolution mapping based on the spatial attraction model by utilizing the pansharpening technique (SRMSAM-PAN) is proposed. In SRMSAM-PAN, a novel processing path, named the pansharpening path, is added to the existing SRMSAM. The original coarse remote sensing image is first fused with the high-resolution panchromatic image from the same area by the pansharpening technique in the novel pansharpening path, and the improved image is unmixed to obtain the novel fine-fraction images. The novel fine-fraction images from the pansharpening path and the existing fine-fraction images from the existing path are then integrated to produce finer-fraction images with more spatial–spectral information. Finally, the values predicted from the finer-fraction images are utilized to allocate class labels to all subpixels, to achieve the final mapping result. Experimental results show that the proposed SRMSAM-PAN can obtain a higher mapping accuracy than the existing SRMSAM methods.
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Bishop, Michael P., Jeffrey S. Kargel, Hugh H. Kieffer, David J. MacKinnon, Bruce H. Raup, and John F. Shroder. "Remote-sensing science and technology for studying glacier processes in high Asia." Annals of Glaciology 31 (2000): 164–70. http://dx.doi.org/10.3189/172756400781820147.
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AbstractA large number of multispectral and stereo-image data are expected to become available as part of the Global Land Ice Measurements from Space project. We investigate digital elevation model extraction, anisotropic reflectance correction and selected glacier analysis tasks that must be developed to achieve full utility of these new data. Results indicate that glaciers in the Karakoram and Nanga Parbat Himalaya, northern Pakistan, exhibit unique spectral, spatial and geomorphometric patterns that can be exploited by various models and algorithms to produce accurate information regarding glacier extent, supraglacial features and glacier geomorphology The integration of spectral, spatial and geomorphometric features, coupled with approaches for advanced pattern recognition, can help geoscientists study glacier mass balance, glacier erosion, sediment-transfer efficiency and landscape evolution.
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Zheng, Cao, Lv, and Benediktsson. "Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images." Remote Sensing 11, no. 16 (August 14, 2019): 1903. http://dx.doi.org/10.3390/rs11161903.
Повний текст джерелаАнотація:
In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial–spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial–spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complement the insufficiency of the spectral feature, and then fused the spatial–spectral features with different strategies. Next, the Manhattan distance between the corresponding spatial–spectral feature vectors of the bi-temporal images was employed to measure the change magnitude between the bi-temporal images and generate a change magnitude image (CMI). Second, the use of the Otsu binary threshold algorithm was proposed to divide the CMI into a binary change detection map (BCDM) and a multi-scale segmentation voting decision algorithm to fuse the initial BCDMs as the final change detection map was proposed. Experiments were carried out on three pairs of bi-temporal remote sensing images with VHR remote sensing images. The results were compared with those of the state-of-the-art methods including four popular contextual-based LCCD methods and three post-processing LCCD methods. Experimental comparisons demonstrated that the proposed approach had an advantage over other state-of-the-art techniques in terms of detection accuracies and performance.
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Liu, Xiao Li. "Object Oriented Information Classification of Remote Sensing Image Based on Segmentation and Merging." Applied Mechanics and Materials 568-570 (June 2014): 734–39. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.734.
Повний текст джерелаАнотація:
The spectral characteristic to classify the remote sensing image classification methods based on pixels of tradition, and the object oriented classification method besides the spectral information, texture feature, also includes the spatial structure of images and other information, so the classification accuracy is very high. In this paper, the remote sensing image based on object oriented classification, puts forward the classification of remote sensing image segmentation based on multiple information combination. Experiments show that, this method can overcome the pixel maximum likelihood classification based on frequent pepper phenomenon of tradition, greatly improves the classification accuracy and reliability. and has better visual effect.
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Li, Sitao, Zhaoming Wang, Shegang Shao, Liuyang Fang, Dan Wang, and Zhiqiang Liu. "Analysis on the Applicability of High-resolution Remote Sensing Images for Highway Construction." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012008. http://dx.doi.org/10.1088/1742-6596/2031/1/012008.
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Abstract This paper first analyzes and determines the scope and contents of highway engineering environmental supervision from the aspects of highway engineering composition, construction stage division, impact environmental factors and their characteristics, environmental protection requirements, etc., combines with the characteristics of high-resolution remote sensing images, proposes the construction process and supervision precision requirements for the highway engineering in which environment protection control can be carried out by using satellite remote-sensing images and UAV (unmanned aerial vehicle) remote-sensing images. Through the environmental investigation experiment of high-resolution satellite remote sensing images commonly used in typical road sections, the satellite remote-sensing images meeting the highway engineering environmental supervision requirements are selected through comparative analysis from the perspectives of spatial resolution, revisit cycle, spectral waveband characteristics, environmental supervision precision requirements and image purchase cost rationality.
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Feng, Xiaoxiao, Luxiao He, Qimin Cheng, Xiaoyi Long, and Yuxin Yuan. "Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information." Remote Sensing 12, no. 6 (March 21, 2020): 1009. http://dx.doi.org/10.3390/rs12061009.
Повний текст джерелаАнотація:
Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS–MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually.
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Teffahi, H., and N. Teffahi. "EMAP-DCNN: A NOVEL MATHEMATICAL MORPHOLOGY AND DEEP LEARNING COMBINED FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 479–86. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-479-2020.
Повний текст джерелаАнотація:
Abstract. The classification of hyperspectral image (HSI) with high spectral and spatial resolution represents an important and challenging task in image processing and remote sensing (RS) domains due to the problem of computational complexity and big dimensionality of the remote sensing images. The spatial and spectral pixel characteristics have crucial significance for hyperspectral image classification and to take into account these two types of characteristics, various classification and feature extraction methods have been developed to improve spectral-spatial classification of remote sensing images for thematic mapping purposes such as agricultural mapping, urban mapping, emergency mapping in case of natural disasters... In recent years, mathematical morphology and deep learning (DL) have been recognized as prominent feature extraction techniques that led to remarkable spectral-spatial classification performances. Among them, Extended Multi-Attribute Profiles (EMAP) and Dense Convolutional Neural Network (DCNN) are considered as robust and powerful approaches such as the work in this paper is based on these two techniques for the feature extraction stage and used in two combined manners and constructing the EMAP-DCNN frame. The experiments were conducted on two popular datasets: “Indian Pines” and “Huston” hyperspectral datasets. Experimental results demonstrate that the two proposed approaches of the EMAP-DCNN frame denoted EMAP-DCNN 1, EMAP-DCNN 2 provide competitive performances compared with some state-of-the-art spectral-spatial classification methods based on deep learning.
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Zhao, Jingzheng, Liyuan Wang, Hui Yang, Penghai Wu, Biao Wang, Chengrong Pan, and Yanlan Wu. "A Land Cover Classification Method for High-Resolution Remote Sensing Images Based on NDVI Deep Learning Fusion Network." Remote Sensing 14, no. 21 (October 30, 2022): 5455. http://dx.doi.org/10.3390/rs14215455.
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High-resolution remote sensing (HRRS) images have few spectra, low interclass separability and large intraclass differences, and there are some problems in land cover classification (LCC) of HRRS images that only rely on spectral information, such as misclassification of small objects and unclear boundaries. Here, we propose a deep learning fusion network that effectively utilizes NDVI, called the Dense-Spectral-Location-NDVI network (DSLN). In DSLN, we first extract spatial location information from NDVI data at the same time as remote sensing image data to enhance the boundary information. Then, the spectral features are put into the encoding-decoding structure to abstract the depth features and restore the spatial information. The NDVI fusion module is used to fuse the NDVI information and depth features to improve the separability of land cover information. Experiments on the GF-1 dataset show that the mean OA (mOA) and the mean value of the Kappa coefficient (mKappa) of the DSLN network model reach 0.8069 and 0.7161, respectively, which have good applicability to temporal and spatial distribution. The comparison of the forest area released by Xuancheng Forestry Bureau and the forest area in Xuancheng produced by the DSLN model shows that the former is consistent with the latter. In conclusion, the DSLN network model is effectively applied in practice and can provide more accurate land cover data for regional ESV analysis.
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Luo, Xiao Qing, and Xiao Jun Wu. "Fusing Remote Sensing Images Using a Statistical Model." Applied Mechanics and Materials 263-266 (December 2012): 416–20. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.416.
Повний текст джерелаАнотація:
Enhance spectral fusion quality is the one of most significant targets in the field of remote sensing image fusion. In this paper, a statistical model based fusion method is proposed, which is the improved method for fusing remote sensing images on the basis of the framework of Principal Component Analysis(PCA) and wavelet decomposition-based image fusion. PCA is applied to the source images. In order to retain the entropy information of data, we select the principal component axes based on entropy contribution(ECA). The first entropy component and panchromatic image(PAN) are performed a multiresolution decompositon using wavelet transform. The low frequency subband fused by weighted aggregation approach and high frequency subband fused by statistical model. High resolution multispectral image is then obtained by an inverse wavelet and ECA transform. The experimental results demonstrate that the proposed method can retain the spectral information and spatial information in the fusion of PAN and multi-spectral image(MS).
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Weber, I., A. Jenal, C. Kneer, and J. Bongartz. "GYROCOPTER-BASED REMOTE SENSING PLATFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1333–37. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1333-2015.
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In this paper the development of a lightweight and highly modularized airborne sensor platform for remote sensing applications utilizing a gyrocopter as a carrier platform is described. The current sensor configuration consists of a high resolution DSLR camera for VIS-RGB recordings. As a second sensor modality, a snapshot hyperspectral camera was integrated in the aircraft. Moreover a custom-developed thermal imaging system composed of a VIS-PAN camera and a LWIR-camera is used for aerial recordings in the thermal infrared range. Furthermore another custom-developed highly flexible imaging system for high resolution multispectral image acquisition with up to six spectral bands in the VIS-NIR range is presented. The performance of the overall system was tested during several flights with all sensor modalities and the precalculated demands with respect to spatial resolution and reliability were validated. The collected data sets were georeferenced, georectified, orthorectified and then stitched to mosaics.
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Li, Feiyan. "Assessment of Multisource Remote Sensing Image Fusion by several dissimilarity Methods." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012016. http://dx.doi.org/10.1088/1742-6596/2031/1/012016.
Повний текст джерелаАнотація:
Abstract Recently, advancements in remote sensing technology have made it easier to obtain various temporal and spatial resolution satellite data. Remote sensing techniques can be a useful tool to detect vegetation and soil conditions, monitor crop diseases and natural disaster prevention, etc. Although the same scene taken by different sensors belong to the same ground object, the information that they offered are redundant, complementary and collaborative due to the spatial, spectral and temporal resolution are different. The method of image fusion can integrate an image with rich details and valuable information from multi-source remote sensing images, which aim to obtain more comprehensive and precise observations of the ground object. By using aspects from multi-source image fusion, this review presents the current status and future trends in remote sensing image fusion. First, different image properties and their applications are presented for remote sensing datasets at home and abroad. Second, a general summary and inductive analysis of the challenging difficulty of different types of multisource image fusion methods is conducted. Third, experiments are tested on eight different methodological approaches, and experimental results demonstrate that GSA method is the best alternative in terms of obtaining high spatial resolution and retaining the spectral information.
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Hnatushenko, V. V., and V. V. Vasyliev. "REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 653–59. http://dx.doi.org/10.5194/isprs-archives-xli-b7-653-2016.
Повний текст джерелаАнотація:
In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.
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Hnatushenko, V. V., and V. V. Vasyliev. "REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 653–59. http://dx.doi.org/10.5194/isprsarchives-xli-b7-653-2016.
Повний текст джерелаАнотація:
In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.
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Koeva, Mila, Rohan Bennett, and Claudio Persello. "Remote Sensing for Land Administration 2.0." Remote Sensing 14, no. 17 (September 2, 2022): 4359. http://dx.doi.org/10.3390/rs14174359.
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Contemporary land administration (LA) systems incorporate the concepts of cadastre and land registration. Conceptually, LA is part of a global land management paradigm incorporating LA functions such as land value, land tenure, land development, and land use. The implementation of land-related policies integrated with well-maintained spatial information reflects the aim set by the United Nations to deliver tenure security for all (Sustainable Development Goal target 1.4, amongst many others). Innovative methods for data acquisition, processing, and maintaining spatial information are needed in response to the global challenges of urbanization and complex urban infrastructure. Current technological developments in remote sensing and geo-spatial information science provide enormous opportunities in this respect. Over the past decade, the increasing usage of unmanned aerial vehicles (UAVs), satellite and airborne-based acquisitions, as well as active remote sensing sensors such as LiDAR, resulted in high spatial, spectral, radiometric, and temporal resolution data. Moreover, significant progress has also been achieved in automatic image orientation, surface reconstruction, scene analysis, change detection, classification, and automatic feature extraction with the help of artificial intelligence, spatial statistics, and machine learning. These technology developments, applied to LA, are now being actively demonstrated, piloted, and scaled. This Special Issue hosts papers focusing on the usage and integration of emerging remote sensing techniques and their potential contribution to the LA domain.
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Zhou, Hui, and Hongmin Gao. "Fusion Method for Remote Sensing Image Based on Fuzzy Integral." Journal of Electrical and Computer Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/437939.
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This paper presents a kind of image fusion method based on fuzzy integral, integrated spectral information, and 2 single factor indexes of spatial resolution in order to greatly retain spectral information and spatial resolution information in fusion of multispectral and high-resolution remote sensing images. Firstly, wavelet decomposition is carried out to two images, respectively, to obtain wavelet decomposition coefficients of the two image and keep coefficient of low frequency of multispectral image, and then optimized fusion is carried out to high frequency part of the two images based on weighting coefficient to generate new fusion image. Finally, evaluation is carried out to the image after fusion with introduction of evaluation indexes of correlation coefficient, mean value of image, standard deviation, distortion degree, information entropy, and so forth. The test results show that this method integrated multispectral information and space high-resolution information in a better way, and it is an effective fusion method of remote sensing image.
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Levy, Joseph, Anne Nolin, Andrew Fountain, and James Head. "Hyperspectral measurements of wet, dry and saline soils from the McMurdo Dry Valleys: soil moisture properties from remote sensing." Antarctic Science 26, no. 5 (February 14, 2014): 565–72. http://dx.doi.org/10.1017/s0954102013000977.
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AbstractSoil moisture is a spatially heterogeneous quantity in the McMurdo Dry Valleys of Antarctica that exerts a large influence on the biological community and on the thermal state of Dry Valleys permafrost. The goal of this project was to determine whether hyperspectral remote sensing techniques could be used to determine soil moisture conditions in the Dry Valleys. We measured the spectral reflectance factors of wetted soil samples from the Dry Valleys under natural light conditions and related diagnostic spectral features to surface layer soil moisture content. Diagnostic water absorption features in the spectra at 1.4 µm and 1.9 µm were present in all samples, including samples doped with high concentrations of chloride salts. The depth of the 1.4 µm absorption is shown to increase linearly with increasing gravimetric water content. These results suggest that airborne hyperspectral imaging of the Dry Valleys could generate soil moisture maps of this environment over large spatial areas using non-invasive remote-sensing techniques.
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Magiera, Janusz. "Can Satellite Remote Sensing be Applied in Geological Mapping in Tropics?" E3S Web of Conferences 35 (2018): 02004. http://dx.doi.org/10.1051/e3sconf/20183502004.
Повний текст джерелаАнотація:
Remote sensing (RS) techniques are based on spectral data registered by RS scanners as energy reflected from the Earth’s surface or emitted by it. In “geological” RS the reflectance (or emittence) should come from rock or sediment. The problem in tropical and subtropical areas is a dense vegetation. Spectral response from the rocks and sediments is gathered only from the gaps among the trees and shrubs. Images of high resolution are appreciated here, therefore. New generation of satellites and scanners (Digital Globe WV2, WV3 and WV4) yield imagery of spatial resolution of 2 m and up to 16 spectral bands (WV3). Images acquired by Landsat (TM, ETM+, OLI) and Sentinel 2 have good spectral resolution too (6–12 bands in visible and infrared) and, despite lower spatial resolution (10–60 m of pixel size) are useful in extracting lithological information too. Lithological RS map may reveal good precision (down to a single rock or outcrop of a meter size). Supplemented with the analysis of Digital Elevation Model and high resolution ortophotomaps (Google Maps, Bing etc.) allows for quick and cheap mapping of unsurveyed areas.
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Mhangara, Paidamwoyo, Willard Mapurisa, and Naledzani Mudau. "Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery." Applied Sciences 10, no. 5 (March 10, 2020): 1881. http://dx.doi.org/10.3390/app10051881.
Повний текст джерелаАнотація:
Preservation of spectral and spatial information is an important requirement for most quantitative remote sensing applications. In this study, we use image quality metrics to evaluate the performance of several image fusion techniques to assess the spectral and spatial quality of pansharpened images. We evaluated twelve pansharpening algorithms in this study; the Local Mean and Variance Matching (IMVM) algorithm was the best in terms of spectral consistency and synthesis followed by the ratio component substitution (RCS) algorithm. Whereas the IMVM and RCS image fusion techniques showed better results compared to other pansharpening methods, it is pertinent to highlight that our study also showed the credibility of other pansharpening algorithms in terms of spatial and spectral consistency as shown by the high correlation coefficients achieved in all methods. We noted that the algorithms that ranked higher in terms of spectral consistency and synthesis were outperformed by other competing algorithms in terms of spatial consistency. The study, therefore, concludes that the selection of image fusion techniques is driven by the requirements of remote sensing application and a careful trade-off is necessary to account for the impact of scene radiometry, image sharpness, spatial and spectral consistency, and computational overhead.
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Dou, Xinyu, Chenyu Li, Qian Shi, and Mengxi Liu. "Super-Resolution for Hyperspectral Remote Sensing Images Based on the 3D Attention-SRGAN Network." Remote Sensing 12, no. 7 (April 8, 2020): 1204. http://dx.doi.org/10.3390/rs12071204.
Повний текст джерелаАнотація:
Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying and analyzing targets in HSIs. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. To better restore the spectral information in the HSI SR field, a novel super-resolution (SR) method was proposed in this study. Firstly, we innovatively used three-dimensional (3D) convolution based on SRGAN (Super-Resolution Generative Adversarial Network) structure to not only exploit the spatial features but also preserve spectral properties in the process of SR. Moreover, we used the attention mechanism to deal with the multiply features from the 3D convolution layers, and we enhanced the output of our model by improving the content of the generator’s loss function. The experimental results indicate that the 3DASRGAN (3D Attention-based Super-Resolution Generative Adversarial Network) is both visually quantitatively better than the comparison methods, which proves that the 3DASRGAN model can reconstruct high-resolution HSIs with high efficiency.
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Zhao, Ji, Yanfei Zhong, Xin Hu, Lifei Wei, and Liangpei Zhang. "A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions." Remote Sensing of Environment 239 (March 2020): 111605. http://dx.doi.org/10.1016/j.rse.2019.111605.
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Gao, Yunhao, Xiukai Song, Wei Li, Jianbu Wang, Jianlong He, Xiangyang Jiang, and Yinyin Feng. "Fusion Classification of HSI and MSI Using a Spatial-Spectral Vision Transformer for Wetland Biodiversity Estimation." Remote Sensing 14, no. 4 (February 11, 2022): 850. http://dx.doi.org/10.3390/rs14040850.
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The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale. Therefore, accurate land-cover mapping is the precondition of biodiversity estimation. However, the environment of the wetlands is complex, and the vegetation is mixed and patchy, so the land-cover recognition based on remote sensing is full of challenges. This paper constructs a systematic framework for multisource remote sensing image processing. Firstly, the hyperspectral image (HSI) and multispectral image (MSI) are fused by the CNN-based method to obtain the fused image with high spatial-spectral resolution. Secondly, considering the sequentiality of spatial distribution and spectral response, the spatial-spectral vision transformer (SSViT) is designed to extract sequential relationships from the fused images. After that, an external attention module is utilized for feature integration, and then the pixel-wise prediction is achieved for land-cover mapping. Finally, land-cover mapping and benthos data at the sites are analyzed consistently to reveal the distribution rule of benthos. Experiments on ZiYuan1-02D data of the Yellow River estuary wetland are conducted to demonstrate the effectiveness of the proposed framework compared with several related methods.
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Ren, Yuanyuan, Xianfeng Zhang, Yongjian Ma, Qiyuan Yang, Chuanjian Wang, Hailong Liu, and Quan Qi. "Full Convolutional Neural Network Based on Multi-Scale Feature Fusion for the Class Imbalance Remote Sensing Image Classification." Remote Sensing 12, no. 21 (October 29, 2020): 3547. http://dx.doi.org/10.3390/rs12213547.
Повний текст джерелаАнотація:
Remote sensing image segmentation with samples imbalance is always one of the most important issues. Typically, a high-resolution remote sensing image has the characteristics of high spatial resolution and low spectral resolution, complex large-scale land covers, small class differences for some land covers, vague foreground, and imbalanced distribution of samples. However, traditional machine learning algorithms have limitations in deep image feature extraction and dealing with sample imbalance issue. In the paper, we proposed an improved full-convolution neural network, called DeepLab V3+, with loss function based solution of samples imbalance. In addition, we select Sentinel-2 remote sensing images covering the Yuli County, Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China as data sources, then a typical region image dataset is built by data augmentation. The experimental results show that the improved DeepLab V3+ model can not only utilize the spectral information of high-resolution remote sensing images, but also consider its rich spatial information. The classification accuracy of the proposed method on the test dataset reaches 97.97%. The mean Intersection-over-Union reaches 87.74%, and the Kappa coefficient 0.9587. The work provides methodological guidance to sample imbalance correction, and the established data resource can be a reference to further study in the future.
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Pereira, Eveline, Eduarda Silveira, Inácio Thomaz Bueno, and Fausto Weimar Acerbi Júnior. "Spatial and spectral remote sensing features to detect deforestation in Brazilian Savannas." Advances in Forestry Science 6, no. 4 (December 30, 2019): 775. http://dx.doi.org/10.34062/afs.v6i4.7525.
Повний текст джерелаАнотація:
The Brazilian Savannas have been under increasing anthropic pressure for many years, and land-use/land-cover changes (LULCC) have been largely neglected. Remote sensing provides useful tools to detect changes, but previous studies have not attempted to separate the effects of phenology from deforestation, clearing or fires to improve the accuracy of change detection without a dense time series. The scientific questions addressed in this study were: how well can we differentiate seasonal changes from deforestation processes combining the spatial and spectral information of bi-temporal (normalized difference vegetation index) NDVI images? Which feature best contribute to increase the separability on classification assessment? We applied an object-based remote sensing method that is able to separate seasonal changes due to phenology effects from LULCC by combining spectral and the spatial context using traditional spectral features and semivariogram indices, exploring the full capability of NDVI image difference to train random forest (RF) algorithm. We found that the spatial variability of NDVI values is not affect by vegetation seasonality and, therefore, the combination of spectral features and semivariogram indices provided high global accuracy (97.73%) to separate seasonal changes and deforestation or fires. From the total of 13 features, 6 provided the best combination to increase the separability on classification assessment (4 spatial and 2 spectral features). How to accurately extract LULCC while disregarding the ones caused by phenological differences in Brazilian seasonal biomes undergoing rapid land-cover changes can be achieved by adding semivariogram indices in combination with spectral features as input data to train RF algorithm.
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Wang, Guizhou, Jianbo Liu, and Guojin He. "A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification." Scientific World Journal 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/192982.
Повний текст джерелаАнотація:
This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.
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Li, C. K., W. Fang, and X. J. Dong. "Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (June 26, 2015): 75–80. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-75-2015.
Повний текст джерелаАнотація:
With the development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing data is greatly improved. How to efficiently process and interpret the massive high resolution remote sensing image data for ground objects, which with spatial geometry and texture information, has become the focus and difficulty in the field of remote sensing research. An object oriented and rule of the classification method of remote sensing data has presents in this paper. Through the discovery and mining the rich knowledge of spectrum and spatial characteristics of high-resolution remote sensing image, establish a multi-level network image object segmentation and classification structure of remote sensing image to achieve accurate and fast ground targets classification and accuracy assessment. Based on worldview-2 image data in the Zangnan area as a study object, using the object-oriented image classification method and rules to verify the experiment which is combination of the mean variance method, the maximum area method and the accuracy comparison to analysis, selected three kinds of optimal segmentation scale and established a multi-level image object network hierarchy for image classification experiments. The results show that the objectoriented rules classification method to classify the high resolution images, enabling the high resolution image classification results similar to the visual interpretation of the results and has higher classification accuracy. The overall accuracy and Kappa coefficient of the object-oriented rules classification method were 97.38%, 0.9673; compared with object-oriented SVM method, respectively higher than 6.23%, 0.078; compared with object-oriented KNN method, respectively more than 7.96%, 0.0996. The extraction precision and user accuracy of the building compared with object-oriented SVM method, respectively higher than 18.39%, 3.98%, respectively better than the object-oriented KNN method 21.27%, 14.97%.
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Li, Weisheng, Xuesong Liang, and Meilin Dong. "MDECNN: A Multiscale Perception Dense Encoding Convolutional Neural Network for Multispectral Pan-Sharpening." Remote Sensing 13, no. 3 (February 2, 2021): 535. http://dx.doi.org/10.3390/rs13030535.
Повний текст джерелаАнотація:
With the rapid development of deep neural networks in the field of remote sensing image fusion, the pan-sharpening method based on convolutional neural networks has achieved remarkable effects. However, because remote sensing images contain complex features, existing methods cannot fully extract spatial features while maintaining spectral quality, resulting in insufficient reconstruction capabilities. To produce high-quality pan-sharpened images, a multiscale perception dense coding convolutional neural network (MDECNN) is proposed. The network is based on dual-stream input, designing multiscale blocks to separately extract the rich spatial information contained in panchromatic (PAN) images, designing feature enhancement blocks and dense coding structures to fully learn the feature mapping relationship, and proposing comprehensive loss constraint expectations. Spectral mapping is used to maintain spectral quality and obtain high-quality fused images. Experiments on different satellite datasets show that this method is superior to the existing methods in both subjective and objective evaluations.