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Journal articles on the topic 'Remote Sensing Image Data Analysis'
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Fu, N., L. Sun, H. Z. Yang, J. Ma, and B. Q. Liao. "RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 565–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-565-2020.
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Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.
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Li, Runya, and Shenglian Li. "Multimedia Image Data Analysis Based on KNN Algorithm." Computational Intelligence and Neuroscience 2022 (April 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/7963603.
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In order to improve the authenticity of multispectral remote sensing image data analysis, the KNN algorithm and hyperspectral remote sensing technology are used to organically combine advanced multimedia technology with spectral technology to subdivide the spectrum. Different classification methods are used to classify CHRIS 0°, and the results are analyzed and compared: SVM classification accuracy is the highest 72 8448%, Kappa coefficient is 0.6770, and SVM is used to classify CHRIS images from five angles, and the results are compared and analyzed: the classification accuracy is from high to low, and the order is FZA = 0 > FZA = −36 > FZA = −55 > FZA = 36 > FZA = 55; SVM is used to classify the multiangle combined image, and the result is compared with the CHRIS 0° result: the overall classification accuracy of angle-combined image types is lower than that of single-angle images; the SVM is used to classify the band-combined image, and the result is compared with CHRIS 0°: the overall classification accuracy of the band combination image forest type is very low, and the effect is not as good as the combining multiangle image classification results. It is verified that if CHRIS multiangle hyper-spectral data are used for classification, the SVM method should be used to classify spectral remote sensing image data with the best effect.
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Karimov, B., G. Karimova, and N. Amankulova. "Land Cover Classification Improvements by Remote Sensing Data Fusion." Bulletin of Science and Practice, no. 2 (February 15, 2023): 66–74. http://dx.doi.org/10.33619/2414-2948/87/07.
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Computer processing and analysis of satellite data is an urgent task of the science of remote sensing of the earth. Such processing can range from adjusting the contrast and brightness of the images of an amateur photographer to a group of scientists using neural network classification to determine the types of minerals in a hyperspectral satellite image. This article implements a method of satellite data fusion, which improves the digital image interpretation and image quality for further analysis. For fusion, a multispectral image with a resolution of 30 m Landsat 5 with 6 channels was taken, with three more significant and informative in their composition were used, as well as a panchromatic (monochrome) image with a resolution of 15 m. To evaluate the resolution of the images and the resulting images before and after the image fusion algorithm, image slices along a straight line and intersecting buildings, green mass, roads and industrial areas presented. For testing, test territories taken from Google Earth and the field work results.
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Veljanovski, Tatjana, Urša Kanjir, and Krištof Oštir. "Object-based image analysis of remote sensing data." Geodetski vestnik 55, no. 04 (2011): 641–64. http://dx.doi.org/10.15292/geodetski-vestnik.2011.04.641-664.
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Bazi, Yakoub, Gabriele Cavallaro, Begüm Demir, and Farid Melgani. "Learning from Data for Remote Sensing Image Analysis." International Journal of Remote Sensing 43, no. 15-16 (August 18, 2022): 5527–33. http://dx.doi.org/10.1080/01431161.2022.2131481.
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Lukáš Brodský and Luboš, Borůvka. "Object-oriented Fuzzy Analysis of Remote Sensing Data for Bare Soil Brightness Mapping." Soil and Water Research 1, No. 3 (January 7, 2013): 79–84. http://dx.doi.org/10.17221/6509-swr.
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Remote sensing data have an important advantage; the data provide spatially exhaustive sampling of the area of interest instead of having samples of tiny fractions. Vegetation cover is, however, one of the application constraints in soil science. Areas of bare soil can be mapped. These spatially dense data require proper techniques to map identified patterns. The objective of this study was mapping of spatial patterns of bare soil colour brightness in a Landsat 7 satellite image in the study area of Central Bohemia using object-oriented fuzzy analysis. A soil map (1:200 000) was used to associate soil types with the soil brightness in the image. Several approaches to determine membership functions (MF) of the fuzzy rule base were tested. These included a simple manual approach, k-means clustering, a method based on the sample histogram, and one using the probability density function. The method that generally provided the best results for mapping the soil brightness was based on the probability density function with KIA = 0.813. The resulting classification map was finally compared with an existing soil map showing 72.0% agreement of the mapped area. The disagreement of 28.0% was mainly in the areas of Chernozems (69.3%).
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Yu, Songyi, and Guotao Wang. "Study on the example segmentation method of remote sensing image based on neural network." Advances in Engineering Technology Research 6, no. 1 (June 12, 2023): 129. http://dx.doi.org/10.56028/aetr.6.1.129.2023.
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With the rapid development of modern science and technology, people collect remote sensing data from the altitude perspective at the same time, put forward the use of a variety of remote sensing images to solve the military exploration, meteorological analysis, environmental protection, resource exploration and other basic problems. However, since remote sensing images have the characteristics of too large data, high image resolution and extremely low application efficiency, some scholars have used the image features in residual network problems in their research to solve the problem of remote sensing image target segmentation scale difference based on the attention mechanism and single-step case segmentation framework. In this paper, based on the understanding of the research status of neural networks and remote sensing image application, a remote sensing image segmentation model based on multi-level channel attention is proposed according to the model architecture of convolutional neural networks. The final experimental results show that the neural network based remote sensing image case segmentation technology has positive effects.
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Khare, Smriti. "Remote Sensing Imagery Sensors and Image Interpretation." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 606–7. http://dx.doi.org/10.22214/ijraset.2021.38019.
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Abstract: Remote sensing a universal term that represents the activity of gaining data of an object with a sensor that is genuinely away from the item from an aircraft or satellite. Special cameras are used to gather remotely sensed picture which help the analyst to sense the things about the earth. Remote sensing makes it probable to assemble data of risky or unapproachable zones. Remote sensing data allows researchers to examine the biosphere's biotic and abiotic segments. Remote sensing is used in various fields to acquire the data which is widely used in Geographical Information System. Image interpretation is most basic feature of remote sensing technology. Image interpretation is a process of recognizing the images and collect information for multiple uses. The photographs are usually taken by satellite or aircrafts. Keywords: Image interpretation, image interpretation devices, sensor, remote sensing, data analysis.
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Hutapea, Destri Yanti, and Octaviani Hutapea. "WATERMARKING METHOD OF REMOTE SENSING DATA USING STEGANOGRAPHY TECHNIQUE BASED ON LEAST SIGNIFICANT BIT HIDING." International Journal of Remote Sensing and Earth Sciences (IJReSES) 15, no. 1 (July 6, 2018): 63. http://dx.doi.org/10.30536/j.ijreses.2018.v15.a2824.
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Remote sensing satellite imagery is currently needed to support the needs of information in various fields. Distribution of remote sensing data to users is done through electronic media. Therefore, it is necessary to make security and identity on remote sensing satellite images so that its function is not misused. This paper describes a method of adding confidential information to medium resolution remote sensing satellite images to identify the image using steganography technique. Steganography with the Least Significant Bit (LSB) method is chosen because the insertion of confidential information on the image is performed on the rightmost bits in each byte of data, where the rightmost bit has the smallest value. The experiment was performed on three Landsat 8 images with different area on each composite band 4,3,2 (true color) and 6,5,3 (false color). Visually the data that has been inserted information does not change with the original data. Visually, the image that has been inserted with confidential information (or stego image) is the same as the original image. Both images cannot be distinguished on histogram analysis. The Mean Squared Error value of stego images of all three data less than 0.053 compared with the original image. This means that information security with steganographic techniques using the ideal LSB method is used on remote sensing satellite imagery.
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Tang, Yang, Jiongchao Yan, Yueqi Wu, Jie Hong, Lei Xu, and Zhangrui Lin. "Design of Remote Sensing Image Data Analysis and Processing Platform Based on Environmental Monitoring." Journal of Physics: Conference Series 2136, no. 1 (December 1, 2021): 012056. http://dx.doi.org/10.1088/1742-6596/2136/1/012056.
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Abstract In the continuous innovation of modern technology concept, remote sensing technology as an advanced and practical comprehensive detection technology has been widely used in many fields. Especially for environmental monitoring, the rational use of remote sensing image data analysis and processing platform can not only obtain valuable environmental information, but also provide effective management decisions for climate changeable natural disasters and other issues. Therefore, on the basis of understanding the design scheme of remote sensing image data analysis and processing platform system, this paper makes clear the positive role of remote sensing image processing technology in the development of environmental monitoring based on the application of the platform.
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Wang, Li, Wenhao Li, Xiaoyi Wang, and Jiping Xu. "Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities." PeerJ Computer Science 9 (April 26, 2023): e1292. http://dx.doi.org/10.7717/peerj-cs.1292.
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Background As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. It is proposed in this article to develop a water quality remote sensing image analysis and prediction method based on the improved Pix2Pix (3D-GAN) model to overcome the problems associated with water environment prediction of smart cities based on remote sensing image data having low accuracy in predicting image information, as well as being difficult to train. Methods Firstly, due to inversion differences and weather conditions, water quality remote sensing images are not perfect, which leads to the creation of time series data that cannot be used directly in prediction modeling. Therefore, a method for preprocessing time series of remote sensing images has been proposed in this article. The original remote sensing image was unified by pixel substitution, the image was repaired by spatial weight matrix, and the time series data was supplemented by linear interpolation. Secondly, in order to enhance the ability of the prediction model to process spatial-temporal data and improve the prediction accuracy of remote sensing images, the convolutional gated recurrent unit network is concatenated with the U-net network as the generator of the improved Pix2Pix model. At the same time, the channel attention mechanism is introduced into the convolutional gated recurrent unit network to enhance the ability of extracting image time series information, and the residual structure is introduced into the downsampling of the U-net network to avoid gradient explosion or disappearance. After that, the remote sensing images of historical moments are superimposed on the channels as labels and sent to the discriminator for adversarial training. The improved Pix2Pix model no longer translates images, but can predict two dimensions of space and one dimension of time, so it is actually a 3D-GAN model. Third, remote sensing image inversion data of chlorophyll-a concentrations in the Taihu Lake basin are used to verify and predict the water environment at future moments. Results The results show that the mean value of structural similarity, peak signal-to-noise ratio, cosine similarity, and mutual information between the predicted value of the proposed method and the real remote sensing image is higher than that of existing methods, which indicates that the proposed method is effective in predicting water environment of smart cities.
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Kuznetsov, A. V., and M. V. Gashnikov. "Remote sensing data retouching based on image inpainting algorithms in the forgery generation problem." Computer Optics 44, no. 5 (October 2020): 763–71. http://dx.doi.org/10.18287/2412-6179-co-721.
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We investigate image retouching algorithms for generating forgery Earth remote sensing data. We provide an overview of existing neural network solutions in the field of generation and inpainting of remote sensing images. To retouch Earth remote sensing data, we use imageinpainting algorithms based on convolutional neural networks and generative-adversarial neural networks. We pay special attention to a generative neural network with a separate contour prediction block that includes two series-connected generative-adversarial subnets. The first subnet inpaints contours of the image within the retouched area. The second subnet uses the inpainted contours to generate the resulting retouch area. As a basis for comparison, we use exemplar-based algorithms of image inpainting. We carry out computational experiments to study the effectiveness of these algorithms when retouching natural data of remote sensing of various types. We perform a comparative analysis of the quality of the algorithms considered, depending on the type, shape and size of the retouched objects and areas. We give qualitative and quantitative characteristics of the efficiency of the studied image inpainting algorithms when retouching Earth remote sensing data. We experimentally prove the advantage of generative-competitive neural networks in the construction of forgery remote sensing data.
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Li, J., J. Sheng, Y. Chen, L. Ke, N. Yao, Z. Miao, X. Zeng, L. Hu, and Q. Wang. "A WEB-BASED LEARNING ENVIRONMENT OF REMOTE SENSING EXPERIMENTAL CLASS WITH PYTHON." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2020 (August 24, 2020): 57–61. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2020-57-2020.
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Abstract. Remote sensing course is a general disciplinary required course of human geography and urban-rural planning major. Its class hour is 48, including theoretical classes and experimental classes. Rapid technological developments is remote sensing area demand quick and steady changes in the education programme and its realization, especially in experimental classes. Experimental classes include: introduction to remote sensing software and basic operations, remote sensing data pre-processing (input, output, 2D and 3D terrain display, image cut, image mosaic, and projection transformation), remote sensing image enhancement, remote sensing image transformation, computer aided classification, image interpretation, and remote sensing image terrain analysis. There are two difficulties in the remote sensing experimental classes. First, it cost a lot of time to prepare the remote sensing software and the remote sensing images. Second, some students just want to use the remote sensing as a tool to investigate environment changing, some other students may want to study more remote sensing image processing technologies. A web-based learning environment of remote sensing is developed to facilitate the application of remote sensing experimental teaching. To make the learning more effective, there are eight modules including four optional modules. The Python programming language is chosen to implement the web-based remote sensing learning environment. The web-based learning environment is implemented in a local network server, including the remote sensing data processing algorithms and many satellite image data. Students can easily exercise the remote sensing experimental courses by connecting to the local network server. It is developed mainly for remote sensing experimental course, and also can be adopted by digital image processing or other courses. The feature of web-based learning may be very useful as the online education adopted because of Corona Virus Disease 2019. The results are encouraging and some recommendations will be extracted for the future.
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Shu, Chang, and Lihui Sun. "Automatic target recognition method for multitemporal remote sensing image." Open Physics 18, no. 1 (June 5, 2020): 170–81. http://dx.doi.org/10.1515/phys-2020-0015.
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AbstractThe traditional target recognition method for the remote sensing image is difficult to accurately identify the specified targets from the massive remote sensing image data. Based on the theory of multitemporal recognition, an automatic target recognition method for the remote sensing image is proposed in this article. The proposed recognition method includes four modules: automatic segmentation of multitemporal remote sensing image, automatic target extraction of multitemporal remote sensing image, automatic processing of multitemporal remote sensing image, and automatic recognition of multitemporal remote sensing image. The automatic segmentation of the image target is introduced. The effectiveness of the segmentation technology is verified through the kernel function bandwidth algorithm. Linear feature extraction is used to extract the segmented image. The image extraction processing is described, which includes image profile analysis, image preprocessing, image feature analysis, the region of interest localization, image enhancement processing, recognition processing, and result output. According to the theory of pattern recognition, three different feature recognition images are given, which are partial separable recognition, weakly separable recognition, and fully separable recognition, and then, a new image recognition method is designed. To verify the practical application effect of the recognition method, the proposed method is compared with the traditional recognition method. Experimental results show that the proposed method can accurately identify the specified objects from the massive remote sensing image data and has a high potential for development. This article has an important guiding significance for image recognition.
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Seidlova, Anna, Maria Kudelcikova, Jan Mihalik, and Donatas Rekus. "Interpretation of Remote Sensing Imagery." IOP Conference Series: Earth and Environmental Science 906, no. 1 (November 1, 2021): 012070. http://dx.doi.org/10.1088/1755-1315/906/1/012070.
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Abstract The process of interpretation and analysis of remote sensing data is based on extracting meaningful information from satellite imagery. The quality of visual interpretation depends on the resolvability and recognisability of the main visual characteristics of each photo or images. The current process of image analysis is based on digital processing limited by used satellite or airborne sensors.
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Zhang, Yun, Xueming Li, Jianli Zhang, and Derui Song. "A Study on Coastline Extraction and Its Trend Based on Remote Sensing Image Data Mining." Abstract and Applied Analysis 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/693194.
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In this paper, data mining theory is applied to carry out the field of the pretreatment of remote sensing images. These results show that it is an effective method for carrying out the pretreatment of low-precision remote sensing images by multisource image matching algorithm with SIFT operator, geometric correction on satellite images at scarce control points, and other techniques; the result of the coastline extracted by the edge detection method based on a chromatic aberration Canny operator has a height coincident with the actual measured result; we found that the coastline length of China is predicted to increase in the future by using the grey prediction method, with the total length reaching up to 19,471,983 m by 2015.
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Alokhina, O. V., D. V. Ivchenko, and N. A. Pits. "Thermal remote sensing data analysis in monitoring of natural objects." Information extraction and processing 2020, no. 48 (December 21, 2020): 61–71. http://dx.doi.org/10.15407/vidbir2020.48.061.
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Today, the geographical interpretation of thermal satellite images, by the number of processing methods and applications, remains one of the least deeply studied areas. Geographic objects are characterized by different thermal and radiation properties. Therefore, they react differently to changes in the intensity of solar radiation, which is recorded in thermal images by differences in image brightness. What this article deals with is the usage of thermal satellite images from TIRS system of Landsat 8 in the monitoring of natural objects. Thermal images are a special source of geographical information that reflects the actual thermal radiation of objects on the earth's surface. It’s been defined that the thermal field of natural territories characterizes by high seasonal spatial-temporal variability. So, seasonal dynamics of the intensity of thermal radiation of natural have characteristic differences. It’s defined that winter characterizes by weak contrasts in the intensity of thermal radiation. Water bodies are best identified during this period. For spring, the increased intensity is observed for open woodless areas, in summer for agricultural lands, and in autumn the highest level of thermal radiation intensity is observed within open ground areas. Also, it was determined that the seasonal variability of thermal radiation intensity of different objects shows regularities related to the features of these objects. In other words, it can be their interpretation feature. The structure of the thermal field of protected areas was defined according to the unsupervised classification of a multitemporal thermal image using the IsoCluster algorithm. The accuracy of the performed classification was proved by the full compatibility of classified elements of thermal structure with natural objects.
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Wu, H., and K. Fu. "A MANAGEMENT OF REMOTE SENSING BIG DATA BASE ON STANDARD METADATA FILE AND DATABASE MANAGEMENT SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 653–57. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-653-2020.
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Abstract. As a kind of information carrier which is high capacity, remarkable reliability, easy to obtain and the other features，remote sensing image data is widely used in the fields of natural resources survey, monitoring, planning, disaster prevention and the others (Huang, Jie, et al, 2008). Considering about the daily application scenario for the remote sensing image in professional departments, the demand of usage and management of remote sensing big data is about to be analysed in this paper.In this paper, by combining professional department scenario, the application of remote sensing image analysis of remote sensing data in the use and management of professional department requirements, on the premise of respect the habits, is put forward to remote sensing image metadata standard for reference index, based on remote sensing image files and database management system, large data serialization of time management methods, the method to the realization of the design the metadata standard products, as well as to the standard of metadata content indexed storage of massive remote sensing image database management.
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He, Yan, Kebin Jia, and Zhihao Wei. "Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique." Remote Sensing 15, no. 9 (May 5, 2023): 2412. http://dx.doi.org/10.3390/rs15092412.
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Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately.
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PARANHOS FILHO, ANTONIO C., ALEXIS R. NUMMER, EDILCE A. ALBREZ, ALISSON A. RIBEIRO, and ROMULO MACHADO. "A study of structural lineaments in Pantanal (Brazil) using remote sensing data." Anais da Academia Brasileira de Ciências 85, no. 3 (September 2013): 913–22. http://dx.doi.org/10.1590/s0001-37652013000300007.
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This paper presents a study of the structural lineaments of the Pantanal extracted visually from satellite images (CBERS-2B satellite, Wide Field Imager sensor, a free image available in INTERNET) and a comparison with the structural lineaments of Precambrian and Paleozoic rocks surrounding the Cenozoic Pantanal Basin. Using a free software for satellite image analysis, the photointerpretation showed that the NS, NE and NW directions observed on the Pantanal satellite images are the same recorded in the older rocks surrounding the basin, suggesting reactivation of these basement structural directions during the Quaternary. So the Pantanal Basin has an active tectonics and its evolution seems to be linked to changes that occurred during the Andean subduction.
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Liu, Zefei, Jinqing Li, Xiaoqiang Di, Zhenlong Man, and Yaohui Sheng. "A Novel Multiband Remote-Sensing Image Encryption Algorithm Based on Dual-Channel Key Transmission Model." Security and Communication Networks 2021 (November 18, 2021): 1–27. http://dx.doi.org/10.1155/2021/9698371.
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With the rapid development of remote sensing technology, satellite remote sensing images have been involved in many areas of people’s lives. Remote sensing images contain military secrets, land profiles, and other sensitive data, so it is urgent to encrypt remote sensing images. This paper proposes a dual-channel key transmission model. The plaintext related key is embedded into the ciphertext image through bit-level key hiding transmission strategy, which enhanced the ability of ciphertext image to resist known-plaintext attack and chosen plaintext attack. In addition, a multiband remote sensing image encryption algorithm based on Boolean cross-scrambling and semi-tensor product diffusion is designed. Firstly, the pixel positions of each band of the remote sensing image are disturbed. Then, the random sequence generated by the four-dimensional chaotic system is processed and deformed to obtain a Boolean matrix. Based on the generated Boolean matrix and certain rules, the cross-confusion between the bands is carried out. Finally, the semi-tensor product operation is used in the diffusion process. Simulation results and experimental analysis show that the proposed algorithm obtains a larger key space and has stronger antiattack ability than other remote sensing image encryption algorithms. It can meet the security transmission of multiband remote sensing image in open space.
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Wang, Xiaofeng, and Zhengran Wang. "Analysis and Evaluation of Ecological Environment Monitoring Based on PIE Remote Sensing Image Processing Software." Journal of Robotics 2022 (October 4, 2022): 1–12. http://dx.doi.org/10.1155/2022/1716756.
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With the continuous improvement of people’s demand for ecological environment quality, the research on ecological environment monitoring, analysis and evaluation had been paid more and more attention by relevant departments and personnel. Because the images collected by remote sensing technology were many and multi-source, the features extracted from remote sensing images using traditional methods had been difficult to meet the needs of related industry applications. Therefore, this paper made use of the advantages of PIE remote sensing image processing software in data analysis and processing, and put forward the research on ecological environment monitoring, analysis and evaluation methods. Firstly, on the basis of summarizing the concepts and related problems of ecological environment, this paper analyzed the processing methods of remote sensing data sources of ecological environment, and explained the evaluation standards and common methods of ecological environment. Secondly, the composition of PIE remote sensing image processing technology system and its application advantages were described, the common indicators and analysis methods of ecological environment monitoring were given, and the index system and model of ecological environment comprehensive evaluation were established. Finally, through the analysis of experimental cases, the results showed that the ecological environment monitoring analysis and evaluation method proposed in this paper was feasible. Compared with the traditional methods, the method proposed in this paper could objectively evaluate the ecological environment. This paper can not only provide support for the analysis and processing of remote sensing image data, but also provide an important reference for the application of remote sensing technology in the field of ecological environment.
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Pires de Lima, Rafael, and Kurt Marfurt. "Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis." Remote Sensing 12, no. 1 (December 25, 2019): 86. http://dx.doi.org/10.3390/rs12010086.
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Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinary camera. Natural image datasets can range up to millions of samples and are, therefore, amenable to deep-learning techniques. Many fields of science, remote sensing included, were able to exploit the success of natural image classification by convolutional neural network models using a technique commonly called transfer learning. We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models. We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points. As expected, we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models. Curiously, we find transfer learning from models trained on larger, more generic natural images datasets outperformed transfer learning from models trained directly on smaller remotely sensed datasets. Nonetheless, results show that transfer learning provides a powerful tool for remote-sensing scene classification.
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Salazar, Addisson, Luis Vergara, and Gonzalo Safont. "Editorial for the Special Issue “New Advances on Sub-Pixel Processing: Unmixing and Mapping Methods”." Remote Sensing 13, no. 19 (September 23, 2021): 3807. http://dx.doi.org/10.3390/rs13193807.
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Innovative remote sensing image processing techniques have been progressively studied due to the increasing availability of remote sensing images, powerful techniques of data analysis, and computational power [...]
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Thyrsted, T. "Remote sensing - a new tool in exploration geology." Rapport Grønlands Geologiske Undersøgelse 128 (December 31, 1986): 135–46. http://dx.doi.org/10.34194/rapggu.v128.7930.
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Remote sensing techniques have been applied to mineral exploration in areas of South and East Greenland. The data consist of airborne and satellite-borne (Landsat) multispectral scanner images and geochemical and geophysical measurements interpolated into grid format and registered on the Landsat images. The main image processing methods applied include ratioing, principal component transformation/factor analysis and classification. In addition, visual and subsequent statistical analyses of lineaments were carried out on images from South Greenland. The results of the work include mapping of several hundred spectral anomalies which represent oxidation zones on the ground. The lineament analysis resulted in definition of major linear zones with increased lineament intensities; some of these zones may have geological significance. Supervised classification was carried out on an integrated data set consisting of images and geochemical/geophysical data. The training areas mainly included uranium showings, and the classified image depicts both previously known occurrences and a new area which is statistically similar to the training areas.
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Wang, Ningjun, Tiantian Liu, Xueying Tang, and Qinglie Yuan. "Remote Sensing Satellite Image-Based Monitoring of Agricultural Ecosystem." Wireless Communications and Mobile Computing 2022 (April 9, 2022): 1–12. http://dx.doi.org/10.1155/2022/4235341.
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In the agricultural ecosystem, it is necessary to grasp the differences of soil fertility and crop growth in time and space. With the rapid development of remote sensing and its popularization and application in social production practice, remote sensing has become a new way to obtain farmland information. At present, remote sensing means in the field of agroecosystem monitoring mainly include satellite remote sensing and unmanned aerial remote sensing. It can monitor and manage the agro-ecosystem environment in real time by acquiring remote sensing images. It can monitor the growth and identification of crops, pests, and diseases, and water supply, analysis timely, and effectivity for the spatial information provided by a feedback processing. But its performance needs to be improved. In this paper, the imaging methods and main features of remote sensing images are introduced in order to analyze the characteristics of agricultural ecosystem targets in remote sensing images. Then, based on object-oriented classification technology, a series of farmland images are preprocessed, segmented, and sparsely represented. Processing operations are used to study how to transform crop observation into crop and noncrop discrimination in remote sensing data. The research shows that the effective acquisition of crop image area and the remote sensing monitoring of crop farmland area can be achieved by processing the crop field map image obtained by remote sensing.
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Lijun, ZHAO, and TANG Ping. "Scalability analysis of typical remote sensing data classification methods: A case of remote sensing image scene." National Remote Sensing Bulletin 20, no. 2 (2016): 157–71. http://dx.doi.org/10.11834/jrs.20164279.
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Ferreira, Karine R., Gilberto R. Queiroz, Lubia Vinhas, Rennan F. B. Marujo, Rolf E. O. Simoes, Michelle C. A. Picoli, Gilberto Camara, et al. "Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products." Remote Sensing 12, no. 24 (December 9, 2020): 4033. http://dx.doi.org/10.3390/rs12244033.
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Recently, remote sensing image time series analysis has being widely used to investigate the dynamics of environments over time. Many studies have combined image time series analysis with machine learning methods to improve land use and cover change mapping. In order to support image time series analysis, analysis-ready data (ARD) image collections have been modeled and organized as multidimensional data cubes. Data cubes can be defined as sets of time series associated with spatially aligned pixels. Based on lessons learned in the research project e-Sensing, related to national demands for land use and cover monitoring and related to state-of-the-art studies on relevant topics, we define the requirements to build Earth observation data cubes for Brazil. This paper presents the methodology to generate ARD and multidimensional data cubes from remote sensing images for Brazil. We describe the computational infrastructure that we are developing in the Brazil Data Cube project, composed of software applications and Web services to create, integrate, discover, access, and process the data sets. We also present how we are producing land use and cover maps from data cubes using image time series analysis and machine learning techniques.
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Shi, Qi, Liu, Niu, and Zhang. "Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data." Remote Sensing 11, no. 22 (November 19, 2019): 2719. http://dx.doi.org/10.3390/rs11222719.
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Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, “marks” left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings.
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Veljanovski, Tatjana, Urša Kanjir, and Krištof Oštir. "Objektno usmerjena analiza podatkov daljinskega zaznavanja." Geodetski vestnik 55, no. 04 (2011): 665–88. http://dx.doi.org/10.15292/geodetski-vestnik.2011.04.665-688.
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de Carvalho, Osmar Luiz Ferreira, Osmar Abílio de Carvalho Júnior, Cristiano Rosa e. Silva, Anesmar Olino de Albuquerque, Nickolas Castro Santana, Dibio Leandro Borges, Roberto Arnaldo Trancoso Gomes, and Renato Fontes Guimarães. "Panoptic Segmentation Meets Remote Sensing." Remote Sensing 14, no. 4 (February 16, 2022): 965. http://dx.doi.org/10.3390/rs14040965.
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Panoptic segmentation combines instance and semantic predictions, allowing the detection of countable objects and different backgrounds simultaneously. Effectively approaching panoptic segmentation in remotely sensed data is very promising since it provides a complete classification, especially in areas with many elements as the urban setting. However, some difficulties have prevented the growth of this task: (a) it is very laborious to label large images with many classes, (b) there is no software for generating DL samples in the panoptic segmentation format, (c) remote sensing images are often very large requiring methods for selecting and generating samples, and (d) most available software is not friendly to remote sensing data formats (e.g., TIFF). Thus, this study aims to increase the operability of panoptic segmentation in remote sensing by providing: (1) a pipeline for generating panoptic segmentation datasets, (2) software to create deep learning samples in the Common Objects in Context (COCO) annotation format automatically, (3) a novel dataset, (4) leverage the Detectron2 software for compatibility with remote sensing data, and (5) evaluate this task on the urban setting. The proposed pipeline considers three inputs (original image, semantic image, and panoptic image), and our software uses these inputs alongside point shapefiles to automatically generate samples in the COCO annotation format. We generated 3400 samples with 512 × 512 pixel dimensions and evaluated the dataset using Panoptic-FPN. Besides, the metric analysis considered semantic, instance, and panoptic metrics, obtaining 93.865 mean intersection over union (mIoU), 47.691 Average (AP) Precision, and 64.979 Panoptic Quality (PQ). Our study presents the first effective pipeline for generating panoptic segmentation data for remote sensing targets.
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Li, C. Y., G. Q. Zhou, X. Zhou, and D. Q. Liu. "STUDY AND ANALYSIS OF REMOTE SENSING DATA PARALLEL PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 443–50. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-443-2020.
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Abstract. This paper analyzes a varieties of procedure of remote sensing data processing, and explores the common mathematical models, common algorithm models, and public function processing units of data processing shared by different tasks or even different parts within an individual task. Public modules are established to improve the parallelism of remote sensing data processing based on FPGA, which has excellent parallel processing performance. In addition, in order to reduce the resource consumption and increase the calculation efficiency of the designed FPGA program, the method of avoiding floating-point arithmetic and division operation in FPGA programming are discussed in this paper. There are a large number of common calculation modules between different tasks, such as the rotation matrix calculation module in attitude solution, geometric correction, and orthorectification task. Image preprocessing, feature information extraction, image threshold separation, and connected region markers are all common processing modules for a target detection task. In the same task, there is also a common calculation module. When using the FPGA design program, the power series of 2 can be used to convert the floating-point operation to fixed-point operation with an acceptable precision. A similar approach can transform the division operation into multiplication and shift operations, thereby improve the computational performance of FPGA programming.
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Gloaguen, R., P. R. Marpu, and I. Niemeyer. "Automatic extraction of faults and fractal analysis from remote sensing data." Nonlinear Processes in Geophysics 14, no. 2 (March 22, 2007): 131–38. http://dx.doi.org/10.5194/npg-14-131-2007.
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Abstract. Object-based classification is a promising technique for image classification. Unlike pixel-based methods, which only use the measured radiometric values, the object-based techniques can also use shape and context information of scene textures. These extra degrees of freedom provided by the objects allow the automatic identification of geological structures. In this article, we present an evaluation of object-based classification in the context of extraction of geological faults. Digital elevation models and radar data of an area near Lake Magadi (Kenya) have been processed. We then determine the statistics of the fault populations. The fractal dimensions of fault dimensions are similar to fractal dimensions directly measured on remote sensing images of the study area using power spectra (PSD) and variograms. These methods allow unbiased statistics of faults and help us to understand the evolution of the fault systems in extensional domains. Furthermore, the direct analysis of image texture is a good indicator of the fault statistics and allows us to classify the intensity and type of deformation. We propose that extensional fault networks can be modeled by iterative function system (IFS).
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Liu, Xiao Li. "Commonly Used Correction Model Comparison, Improvement and Precision Analysis of Radiation Remote Sensing Image." Advanced Materials Research 998-999 (July 2014): 1013–17. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1013.
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Remote sensing image radiation correction is a key technique for quantitative remote sensing data processing is essential, especially in the surface undulating mountains, surface radiation affected by topography, the radiation correction of remote sensing inversion error can reduce the surface information, so as to maximize the accuracy of remote sensing investigation of mountain area. In this paper, the ETM image in mountain area of Western Beijing as an example, application of cosine correction model and Sandmeier correction model of image are topographic radiation correction, and then proposed an improved Sandmeier correction model, and carries on the precision analysis from the visual effect and quantitative parameters. Experiments show that, the improved Sandmeier correction model eliminates the influence of topography, greatly improving the accuracy of remote sensing image Topographic Radiation correction.
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Wang, Yani, Jinfang Dong, and Bo Wang. "Feature Matching Optimization of Multimedia Remote Sensing Images Based on Multiscale Edge Extraction." Computational Intelligence and Neuroscience 2022 (June 2, 2022): 1–7. http://dx.doi.org/10.1155/2022/1764507.
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In order to solve the problem of low efficiency of image feature matching in traditional remote sensing image database, this paper proposes the feature matching optimization of multimedia remote sensing images based on multiscale edge extraction, expounds the basic theory of multiscale edge, and then registers multimedia remote sensing images based on the selection of optimal control points. In this paper, 100 remote sensing images with a size of 3619 ∗ 825 with a resolution of 30 m are selected as experimental data. The computer is configured with 2.9 ghz CPU, 16 g memory, and i7 processor. The research mainly includes two parts: image matching efficiency analysis of multiscale model; matching accuracy analysis of multiscale model and formulation of model parameters. The results show that when the amount of image data is large, feature matching takes more time. With the increase of sampling rate, the amount of image data decreases rapidly, and the feature matching time also shortens rapidly, which provides a theoretical basis for the multiscale model to improve the matching efficiency. The data size is the same, 3619 × 1825, which makes the matching time between images have little difference. Therefore, the matching time increases linearly with the increase of the number of images in the database. When the amount of image data in the database is large, a higher number of layers should be used; when the amount of image data in the database is small, the number of layers of the model should be reduced to ensure the accuracy of matching. The availability of the proposed method is proved.
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Setiawan, Kuncoro Teguh, Syifa Wismayati Adawiah, Yennie Marini, and Gathot Winarso. "BATHYMETRY DATA EXTRACTION ANALYSIS USING LANDSAT 8 DATA." International Journal of Remote Sensing and Earth Sciences (IJReSES) 13, no. 2 (June 2, 2017): 79. http://dx.doi.org/10.30536/j.ijreses.2016.v13.a2448.
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The remote sensing technique can be used to produce bathymetric map. Bathymetric mapping is important for the coastal zone and watershed management. In the previous study conducted in Menjangan Island of Bali, bathymetric extractin information from the top of the atmosphere (TOA) reflectance image of Landsat ETM+ data has R2 = 0.620. Not optimal correlation value produced is highly influenced by the reflectance image of Landsat ETM+ data, were used, hence the lack of the research which became the basis of the present study. The study was on the Karang Lebar water of Thousand Islands, Jakarta. And the aim was to determine whether there was an increased correlation coefficient value of bathymetry extraction information generated from Surface reflectance and TOA reflectance imager of Landsat 8 data acquired on August 12, 2014. The method of extraction was done using algorithms Van Hengel and Spitzer (1991). Extraction absolute depth information obtained from the model logarithm of Landsat 8 surface reflectance images and pictures TOA produce a correlation value of R2 = 0.663 and R2 = 0.712.
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Zhang, Zhenxing, Feng Gao, Bin Ma, and Zhiqiang Zhang. "Extraction of Earth Surface Texture Features from Multispectral Remote Sensing Data." Journal of Electrical and Computer Engineering 2018 (October 25, 2018): 1–9. http://dx.doi.org/10.1155/2018/9684629.
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Earth surface texture features referring to as visual features of homogeneity in remote sensing images are very important to understand the relationship between surface information and surrounding environment. Remote sensing data contain rich information of earth surface texture features (image gray reflecting the spatial distribution information of texture features, for instance). Here, we propose an efficient and accurate approach to extract earth surface texture features from remote sensing data, called gray level difference frequency spatial (GLDFS). The gray level difference frequency spatial approach is designed to extract multiband remote sensing data, utilizing principle component analysis conversion to compress the multispectral information, and it establishes the gray level difference frequency spatial of principle components. In the end, the texture features are extracted using the gray level difference frequency spatial. To verify the effectiveness of this approach, several experiments are conducted and indicate that it could retain the coordination relationship among multispectral remote sensing data, and compared with the traditional single-band texture analysis method that is based on gray level co-occurrence matrix, the proposed approach has higher classification precision and efficiency.
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Thuestad, Alma Elizabeth, Ole Risbøl, Jan Ingolf Kleppe, Stine Barlindhaug, and Elin Rose Myrvoll. "Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data." Sustainability 13, no. 4 (February 10, 2021): 1917. http://dx.doi.org/10.3390/su13041917.
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What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.
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Zhang, Kai, Chengquan Hu, and Hang Yu. "Remote Sensing Image Land Classification Based on Deep Learning." Scientific Programming 2021 (December 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/6203444.
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Aiming at the problems of high-resolution remote sensing images with many features and low classification accuracy using a single feature description, a remote sensing image land classification model based on deep learning from the perspective of ecological resource utilization is proposed. Firstly, the remote sensing image obtained by Gaofen-1 satellite is preprocessed, including multispectral data and panchromatic data. Then, the color, texture, shape, and local features are extracted from the image data, and the feature-level image fusion method is used to associate these features to realize the fusion of remote sensing image features. Finally, the fused image features are input into the trained depth belief network (DBN) for processing, and the land type is obtained by the Softmax classifier. Based on the Keras and TensorFlow platform, the experimental analysis of the proposed model shows that it can clearly classify all land types, and the overall accuracy, F1 value, and reasoning time of the classification results are 97.86%, 87.25%, and 128 ms, respectively, which are better than other comparative models.
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Kim, Bumjun, Keunyong Kim, and Joo-Hyung Ryu. "Unmanned aerial vehicle remotely sensed datasets, a reference dataset for coastal topography change and shoreline analysis." GEO DATA 1, no. 1 (December 31, 2019): 38–45. http://dx.doi.org/10.22761/dj2019.01.01.006.
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To analyze tendency of temporal and spatial change of coast using long-term topography and shoreline change data is important. In this study, high-resolution digital elevation model and orthorectified image data were generated using rotary-wing UAV(unmanned aerial vehicle) system for coastal topography and shoreline change analysis. The UAV system has advantage of low cost and high efficiency compared to satellite remote sensing platform so UAV system easily acquire time series image data. The spatial resolution of generated digital elevation model and orthorectified images are very high, in centimeter. Therefore, the above image data can be used in various fields of remote sensing and geography such as detailed coastal topography
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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.
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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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Yusuf, Yuhendra, Josaphat Tetuko Sri Sumantyo, and Hiroaki Kuze. "Spectral information analysis of image fusion data for remote sensing applications." Geocarto International 28, no. 4 (July 2013): 291–310. http://dx.doi.org/10.1080/10106049.2012.692396.
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Saramud, Mikhail V., Igor V. Kovalev, Vasiliy V. Losev, Mariam O. Petrosyan, and Dmitriy I. Kovalev. "Multi-version approach to improve the reliability of processing data of the earth remote sensing in the real-time." E3S Web of Conferences 75 (2019): 01005. http://dx.doi.org/10.1051/e3sconf/20197501005.
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The article describes the use of a multi-version approach to improve the accuracy of the classification of images when solving the problem of image analysis for Earth remote sensing. The implementation of this approach makes it possible to reduce the classification error and, consequently, to increase the reliability of processing remote sensing data. A practical study was carried out in a multi-version real-time execution environment, which makes it possible to organize image processing on board of an unmanned vehicle. The results confirm the effectiveness of the proposed approach.
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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.
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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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Xu, Lewei, Zhuhua Hu, Chong Zhang, and Wei Wu. "Remote Sensing Image Segmentation of Mariculture Cage Using Ensemble Learning Strategy." Applied Sciences 12, no. 16 (August 17, 2022): 8234. http://dx.doi.org/10.3390/app12168234.
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In harbour areas, the irrational layout and high density of mariculture cages can lead to a dramatic deterioration of the culture’s ecology. Therefore, it is important to analyze and regulate the distribution of cages using intelligent analysis based on deep learning. We propose a remote sensing image segmentation method based on the Swin Transformer and ensemble learning strategy. Firstly, we collect multiple remote sensing images of cages and annotate them, while using data expansion techniques to construct a remote sensing image dataset of mariculture cages. Secondly, the Swin Transformer is used as the backbone network to extract the remote sensing image features of cages. A strategy of alternating the local attention module and the global attention module is used for model training, which has the benefit of reducing the attention computation while exchanging global information. Then, the ensemble learning strategy is used to improve the accuracy of remote sensing cage segmentation. We carry out quantitative and qualitative analyses of remote sensing image segmentation of cages at the ports of Li’an, Xincun and Potou in Hainan Province, China. The results show that our proposed segmentation scheme has significant performance improvement compared to other models. In particular, the mIoU reaches 82.34% and pixel accuracy reaches 99.71%.
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Wang, S., and C. Wang. "Research on dimension reduction method for hyperspectral remote sensing image based on global mixture coordination factor analysis." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (June 26, 2015): 159–67. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-159-2015.
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Over the past thirty years, the hyperspectral remote sensing technology is attracted more and more attentions by the researchers. The dimension reduction technology for hyperspectral remote sensing image data is one of the hotspots in current research of hyperspectral remote sensing. In order to solve the problems of nonlinearity, the high dimensions and the redundancy of the bands that exist in the hyperspectral data, this paper proposes a dimension reduction method for hyperspectral remote sensing image data based on the global mixture coordination factor analysis. In the first place, a linear low dimensional manifold is obtained from the nonlinear and high dimensional hyperspectral image data by mixture factor analysis method. In the second place, the parameters of linear low dimensional manifold are estimated by the EM algorithm of find a local maximum of the data log-likelihood. In the third place, the manifold is aligned to a global parameterization by the global coordinated factor analysis model and then the lowdimension image data of hyperspectral image data is obtained at last. Through the comparison of different dimensionality reduction method and different classification method for the low-dimensional data, the result illuminates the proposed method can retain maximum spectral information in hyperspectral image data and can eliminate the redundant among bands.
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Kaur, Amanjot, and Dr Kiran Jyoti. "Reproduction of Remote Sensing Image Using Supervised Mode of Learning Using Artificial Neural Network." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 9 (September 30, 2014): 4799–808. http://dx.doi.org/10.24297/ijct.v13i9.2356.
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Remote sensing is the science of gathering information from a location that is distant from the source. Image analysis is the technique of extracting and interpreting meaningful information from a remotely sensed image. The information from an image may be extracted with the help of computer software or be visually considered. Images like such can be acquired in the form of aerial photograph, a multispectral satellite image, Light Detection and Ranging data, a radar data or a thermal image. Remote sensing is a dynamic technical field of endeavor. This paper is based on the technique involved in mapping of Geographic Information System projects.
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Artemeva, Olga, Aleksandr Bakulev, Natalya Pozdnyakova, and Sergey Tyurin. "Dynamic mapping of disturbed lands using remote sensing data." InterCarto. InterGIS 28, no. 2 (2022): 785–99. http://dx.doi.org/10.35595/2414-9179-2022-2-28-785-799.
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Due to the increase in the areas of disturbed lands, the relevance of developing methods and methods for obtaining and analyzing spatial data in order to make decisions on rational nature management is increasing every year. Monitoring of natural and anthropogenic systems is largely related to the collection, analysis and visualization of dynamic processes, so the technologies for compiling of dynamic maps are at the peak of relevance. A number of factors necessitate the using of dynamic geoimages. Firstly, these images are an inseparable combination of spatial-temporal links on the certain areas. Secondly, it is the possibility of a full-fledged analysis of spatial changes taking into account time. Thirdly, it is the forecasting of natural and socio-economic factors and phenomena. In addition, dynamic mapping opens up opportunities for multimedia data visualization, which increases the observer’s perception of geoimages by several times with the focus on specific objects. Remote sensing data is one of the main sources for compiling and updating thematic dynamic maps. This article demonstrates the development of the method for creating working layers used in geographic information systems (GIS) for compiling dynamic maps using remote sensing data. The authors note a distinctive feature of the methodology: it is aimed at a wide range of users who do not fully have the skills and abilities to work with remote sensing data. These are managers of any level, whose direct work is not related to the compiling of geo-images, but whose competence is to make managerial decisions. Another advantage of the described methods is its implementation in an open source GIS (QGIS), as well as its application not only for single images, but also for a mosaic image. The article presents a description of the entire path from image processing to the creation of visual images. Disturbed lands of the Zabaykalsky Krai of the Russian Federation were chosen as a special example of working polygons. These territories have a large number of environmental problems, causing an increase in the areas of disturbed lands: open pits, an increase in the number of mining and processing factories, degradation of agricultural and forest lands due to anthropogenic activities and erosion processes, active seismic processes, mudflows and avalanche hazard.
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Chu, Boce, Feng Gao, Yingte Chai, Yu Liu, Chen Yao, Jinyong Chen, Shicheng Wang, Feng Li, and Chao Zhang. "Large-Area Full-Coverage Remote Sensing Image Collection Filtering Algorithm for Individual Demands." Sustainability 13, no. 23 (December 6, 2021): 13475. http://dx.doi.org/10.3390/su132313475.
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Remote sensing is the main technical means for urban researchers and planners to effectively observe targeted urban areas. Generally, it is difficult for only one image to cover a whole urban area and one image cannot support the demands of urban planning tasks for spatial statistical analysis of a whole city. Therefore, people often artificially find multiple images with complementary regions in an urban area on the premise of meeting the basic requirements for resolution, cloudiness, and timeliness. However, with the rapid increase of remote sensing satellites and data in recent years, time-consuming and low performance manual filter results have become more and more unacceptable. Therefore, the issue of efficiently and automatically selecting an optimal image collection from massive image data to meet individual demands of whole urban observation has become an urgent problem. To solve this problem, this paper proposes a large-area full-coverage remote sensing image collection filtering algorithm for individual demands (LFCF-ID). This algorithm achieves a new image filtering mode and solves the difficult problem of selecting a full-coverage remote sensing image collection from a vast amount of data. Additionally, this is the first study to achieve full-coverage image filtering that considers user preferences concerning spatial resolution, timeliness, and cloud percentage. The algorithm first quantitatively models demand indicators, such as cloudiness, timeliness, resolution, and coverage, and then coarsely filters the image collection according to the ranking of model scores to meet the different needs of different users for images. Then, relying on map gridding, the image collection is genetically optimized for individuals using a genetic algorithm (GA), which can quickly remove redundant images from the image collection to produce the final filtering result according to the fitness score. The proposed method is compared with manual filtering and greedy retrieval to verify its computing speed and filtering effect. The experiments show that the proposed method has great speed advantages over traditional methods and exceeds the results of manual filtering in terms of filtering effect.
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Wang, Zewei, Pengfei Yang, Haotian Liang, Change Zheng, Jiyan Yin, Ye Tian, and Wenbin Cui. "Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery." Remote Sensing 14, no. 1 (December 23, 2021): 45. http://dx.doi.org/10.3390/rs14010045.
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Forest fire is a ubiquitous disaster which has a long-term impact on the local climate as well as the ecological balance and fire products based on remote sensing satellite data have developed rapidly. However, the early forest fire smoke in remote sensing images is small in area and easily confused by clouds and fog, which makes it difficult to be identified. Too many redundant frequency bands and remote sensing index for remote sensing satellite data will have an interference on wildfire smoke detection, resulting in a decline in detection accuracy and detection efficiency for wildfire smoke. To solve these problems, this study analyzed the sensitivity of remote sensing satellite data and remote sensing index used for wildfire detection. First, a high-resolution remote sensing multispectral image dataset of forest fire smoke, containing different years, seasons, regions and land cover, was established. Then Smoke-Unet, a smoke segmentation network model based on an improved Unet combined with the attention mechanism and residual block, was proposed. Furthermore, in order to reduce data redundancy and improve the recognition accuracy of the algorithm, the conclusion was made by experiments that the RGB, SWIR2 and AOD bands are sensitive to smoke recognition in Landsat-8 images. The experimental results show that the smoke pixel accuracy rate using the proposed Smoke-Unet is 3.1% higher than that of Unet, which could effectively segment the smoke pixels in remote sensing images. This proposed method under the RGB, SWIR2 and AOD bands can help to segment smoke by using high-sensitivity band and remote sensing index and makes an early alarm of forest fire smoke.