Статті в журналах: "Multi-temporal remote sensing"

1

Franke, Jonas, and Gunter Menz. "Multi-temporal wheat disease detection by multi-spectral remote sensing." Precision Agriculture 8, no. 3 (June 24, 2007): 161–72. http://dx.doi.org/10.1007/s11119-007-9036-y.

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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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3

Zhu, Lilu, Xiaolu Su, Yanfeng Hu, Xianqing Tai, and Kun Fu. "A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data." Remote Sensing 13, no. 12 (June 14, 2021): 2333. http://dx.doi.org/10.3390/rs13122333.

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It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

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4

Smith, A. M., D. J. Major, C. W. Lindwall, and R. J. Brown. "Multi-Temporal, Multi-Sensor Remote Sensing for Monitoring Soil Conservation Farming." Canadian Journal of Remote Sensing 21, no. 2 (June 1995): 177–84. http://dx.doi.org/10.1080/07038992.1995.10874611.

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5

Li, Yinshuai, Chunyan Chang, Zhuoran Wang, Tao Li, Jianwei Li, and Gengxing Zhao. "Identification of Cultivated Land Quality Grade Using Fused Multi-Source Data and Multi-Temporal Crop Remote Sensing Information." Remote Sensing 14, no. 9 (April 27, 2022): 2109. http://dx.doi.org/10.3390/rs14092109.

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To explore the fast, accurate, and efficient remote sensing identification method of cultivated land quality, this study took Shandong Province as the study area, and used measured data to carry out the soil quality evaluation based on conventional GIS. On this basis, MODIS sequence images were used as remote sensing data sources, and multi-source data such as topography, meteorology, and statistical yearbook were fused. Then, according to the Pressure-State-Response framework, we constructed three kinds of characteristic indicators through distinguishing crop rotation types and fusing remote sensing data. Finally, the soil quality grade was identified by the random forest method, and the accuracy analysis was carried out. The results showed that the NDVI peak values of double-season crops are in mid-April and mid-August, and one-season crops are in mid-August. Through evaluation, soil quality was divided into three categories, with six grades. Through principal component analysis, each soil status indicator contains two to three principal components, and each principal component contains five to eight temporal crop remote sensing information. After distinguishing crop rotation types and fusing remote sensing images, the identification accuracy of soil quality is significantly improved. The overall accuracy is 79.18%, 86.12%, and 93.65%, and the Kappa coefficient is 0.66, 0.77, and 0.90, respectively. This research developed an automatic identification method for cultivated land quality grade, and it proved that distinguishing crop rotation types and fusing multi-temporal crop remote sensing information are effective ways to improve identification accuracy.

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6

Liu, J., L. Liu, X. Xing, X. Zheng, Y. Gao, Q. Xu, and J. Du. "MULTI-TIER STORAGE MANAGEMENT AND APPLICATION OF REMOTE SENSING IMAGE DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1229–34. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1229-2022.

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Abstract. With the rapid development of the remote sensing platform and sensor technology, remote sensing image data presents the typical characteristics of the massive complex, multi-source heterogeneous, spatial-temporal intensive, which puts forward higher requirements for data storage management efficiency and real-time online service capability. Combined with the demand for remote sensing image data, the multi-tier migration strategy and approach based on the thermal evaluation model of remote sensing image data are proposed, considering the file size and data activity of remote sensing image data. The linkage between local storage cluster and cloud storage implements multi-tier migration and dynamic flow of remote sensing image data, improves the utilization of storage devices and the rationality of storage resource allocation, and enhances the capability of fast, dynamic, and real-time online service of remote sensing image data.

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7

Chen, Peng Xiao, Shao Hong Shen, and Xiong Fei Wen. "Remote Sensing Dynamic Monitoring on Illegal Capacity Occupation of Reservoir." Advanced Materials Research 718-720 (July 2013): 1124–28. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1124.

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Monitoring the illegally occupied channels is very important for the management and regulations of reservoirs. This paper proposes an automatic and efficient approach to identify the changes in the river course with geographic information system and global position system using multi-temporal remote sensing images. Unlike the traditional river course monitoring system, this approach is mainly based on the change detection information extracting from multi-temporal high spatial resolution remote sensing images. Firstly, change detection from different information of multi-temporal remote sensing images are applied to obtain the change information thematic maps which can be used as working maps for on-site investigation are extracted. Secondly, GPS-RTK measurement technology is used to obtain 3-D position information of the terrain features points in those channel occupied areas. Then, an approach for calculating the volume of the channel occupied area is designed and developed by ArcGIS software using multi-temporal remote sensing images, 3-D position information and historical digital terrain date of channel occupied area. Finally, channel occupied area volume data and thematic maps are acquired by ArcGIS software. The data of reservoir is selected as experimental area, and the experiments have confirmed the high efficiency and accuracy of this approach proposed in this paper.

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8

Huang, Fenghua, Zhengyuan Mao, and Wenzao Shi. "ICA-ASIFT-Based Multi-Temporal Matching of High-Resolution Remote Sensing Urban Images." Cybernetics and Information Technologies 16, no. 5 (October 1, 2016): 34–49. http://dx.doi.org/10.1515/cait-2016-0050.

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Abstract While SIFT (Scale Invariant Feature Transform) features are used to match High-Resolution (HR) remote sensing urban images captured at different phases with large scale and view variations, feature points are few and the matching accuracy is low. Although replacing SIFT with fully affine invariant features ASIFT (Affine-SIFT) can increase the number of feature points, it results in matching inefficiency and a non-uniform distribution of matched feature point pairs. To address these problems, this paper proposes the novel matching method ICA-ASIFT, which matches HR remote sensing urban images captured at different phases by using an Independent Component Analysis algorithm (ICA) and ASIFT features jointly. First, all possible affine deformations are modeled for the image transform, extracting ASIFT features of remote sensing images captured at different times. The ICA algorithm reduces the dimensionality of ASIFT features and improves matching efficiency of subsequent ASIFT feature point pairs. Next, coarse matching is performed on ASIFT feature point pairs through the algorithms of Nearest Vector Angle Ratio (NVAR), Direction Difference Analysis (DDA) and RANdom SAmple Consensus (RANSAC), eliminating apparent mismatches. Then, fine matching is performed on rough matched point pairs using a Neighborhoodbased Feature Graph Matching algorithm (NFGM) to obtain final ASIFT matching point pairs of remote sensing images. Finally, final matching point pairs are used to compute the affine transform matrix. Matching HR remote sensing images captured at different phases is achieved through affine transform. Experiments are used to compare the performance of ICA-ASFIT and three other algorithms (i.e., Harris- SIFT, PCA-SIFT, TD-ASIFT) on HR remote sensing images captured at different times in different regions. Experimental results show that the proposed ICA-ASFIT algorithm effectively matches HR remote sensing urban images and outperforms other algorithms in terms of matching accuracy and efficiency.

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9

Xia, Liheng, and Xueying Wu. "A review of hyperspectral remote sensing of crops." E3S Web of Conferences 338 (2022): 01029. http://dx.doi.org/10.1051/e3sconf/202233801029.

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With the development of space science and technology, various resource monitoring environmental satellites provide multi-platform, multi-spectral, multi-temporal and wide-range real-time information for the study of surface dynamic changes, and remote sensing technology has become a powerful technical means for human to study the earth’s resources and environment, while high-resolution and hyperspectral remote sensing has become the main source for fruit tree growth monitoring and fruit quality detection acquisition. This paper has the following aspects to introduce the current situation of application of high-resolution and hyperspectral remote sensing data.

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10

Liu, C., X. Zhou, Y. Zhou, and A. Akbar. "MULTI-TEMPORAL MONITORING OF URBAN RIVER WATER QUALITY USING UAV-BORNE MULTI-SPECTRAL REMOTE SENSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1469–75. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1469-2020.

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Abstract. Water quality is an important index of the ecological environment, which changes rapidly and needs to be monitored chronically. In urban ecological environment, water quality problem is not only more serious, but also more complex in time and space. Remote sensing water quality monitoring can cover a large area in a short time. Therefore, remote sensing can be adopted to make up for the shortcomings of traditional water quality monitoring methods in space coverage and temporal resolution. In order to monitor the narrow rivers in urban area, low altitude remote sensing is needed. This paper proposes a multi-spectral water quality monitoring method based on UAV platform, which can quickly monitor an entire urban water area and conduct multi-temporal observation for key indices of water quality within one day. It is helpful to find and locate the polluted areas which affect the water environment quickly. Also, it can show the changes of water quality on the time axis. The result can provide a decision-making basis for water environment treatment.

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11

Yuhendra and Joshapat Tetuko Sri Sumantyo. "Assessment of Multi-Temporal Image Fusion for Remote Sensing Application." International Journal on Advanced Science, Engineering and Information Technology 7, no. 3 (June 15, 2017): 778. http://dx.doi.org/10.18517/ijaseit.7.3.1676.

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12

Giannopoulos, Michalis, Grigorios Tsagkatakis, and Panagiotis Tsakalides. "4D U-Nets for Multi-Temporal Remote Sensing Data Classification." Remote Sensing 14, no. 3 (January 28, 2022): 634. http://dx.doi.org/10.3390/rs14030634.

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Multispectral sensors constitute a core earth observation imaging technology generating massive high-dimensional observations acquired across multiple time instances. The collected multi-temporal remote sensed data contain rich information for Earth monitoring applications, from flood detection to crop classification. To easily classify such naturally multidimensional data, conventional low-order deep learning models unavoidably toss away valuable information residing across the available dimensions. In this work, we extend state-of-the-art convolutional network models based on the U-Net architecture to their high-dimensional analogs, which can naturally capture multi-dimensional dependencies and correlations. We introduce several model architectures, both of low as well as of high order, and we quantify the achieved classification performance vis-à-vis the latest state-of-the-art methods. The experimental analysis on observations from Landsat-8 reveals that approaches based on low-order U-Net models exhibit poor classification performance and are outperformed by our proposed high-dimensional U-Net scheme.

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13

Yang, Zhuoqian, Tingting Dan, and Yang Yang. "Multi-Temporal Remote Sensing Image Registration Using Deep Convolutional Features." IEEE Access 6 (2018): 38544–55. http://dx.doi.org/10.1109/access.2018.2853100.

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14

Ji, Shunping, Tong Zhang, Qingfeng Guan, and Junli Li. "Nonlinear intensity difference correlation for multi-temporal remote sensing images." International Journal of Applied Earth Observation and Geoinformation 21 (April 2013): 436–43. http://dx.doi.org/10.1016/j.jag.2012.06.009.

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15

Zhang, Hebing, Hongyi Yuan, Weibing Du, and Xiaoxuan Lyu. "Crop Identification Based on Multi-Temporal Active and Passive Remote Sensing Images." ISPRS International Journal of Geo-Information 11, no. 7 (July 11, 2022): 388. http://dx.doi.org/10.3390/ijgi11070388.

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Although vegetation index time series from optical images are widely used for crop mapping, it remains difficult to obtain sufficient time-series data because of satellite revisit time and weather in some areas. To address this situation, this paper considered Wen County, Henan Province, Central China as the research area and fused multi-source features such as backscatter coefficient, vegetation index, and time series based on Sentinel-1 and -2 data to identify crops. Through comparative experiments, this paper studied the feasibility of identifying crops with multi-temporal data and fused data. The results showed that the accuracy of multi-temporal Sentinel-2 data increased by 9.2% compared with single-temporal Sentinel-2 data, and the accuracy of multi-temporal fusion data improved by 17.1% and 2.9%, respectively, compared with multi-temporal Sentinel-1 and Sentinel-2 data. Multi-temporal data well-characterizes the phenological stages of crop growth, thereby improving the classification accuracy. The fusion of Sentinel-1 synthetic aperture radar data and Sentinel-2 optical data provide sufficient time-series data for crop identification. This research can provide a reference for crop recognition in precision agriculture.

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16

Ge, Xingtong, Yi Yang, Jiahui Chen, Weichao Li, Zhisheng Huang, Wenyue Zhang, and Ling Peng. "Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information." Remote Sensing 14, no. 5 (March 1, 2022): 1214. http://dx.doi.org/10.3390/rs14051214.

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Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.

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17

Mirelva, Prima Rizky, and Ryota Nagasawa. "Identification and Classification of Complex Agricultural Croplands Using Multi-Temporal ALOS-2/PALSAR-2 Data: A Case Study in Central Java, Indonesia." Journal of Agricultural Science 10, no. 2 (January 12, 2018): 58. http://dx.doi.org/10.5539/jas.v10n2p58.

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The agriculture sector makes a significant contribution to the Indonesian economy and has become one of the sources of national income. Therefore, precise agricultural mapping is very important to national and regional administrations. Satellite remote sensing provides the most effective tool for identifying a wide expanse of agriculture croplands. However, cloud coverage in tropical regions limits the use of optical remote sensing. SAR is an active remote sensing technique, which offers completely cloud-free observation data. The multi-temporal ALOS-2/PALSAR-2 data were used in this study, complemented by optical multi-temporal remote sensing data, that is, Landsat 8 OLI for classifying complex agricultural croplands. The study area, located in the Klaten Regency, Central Java Province, with 112 km2 coverage, was selected because of its dynamic cropping pattern and complex agricultural land use types. In this study, the RGB composite of HH, HV and HV-HH, derived from ALOS-2/PALSAR-2 polarizations, was found to be effective at separating two types of paddy field cropping pattern: all-year paddy (paddy-I) and paddy upland fields (paddy-II). The multi-temporal Landsat 8 data were also found to be useful for observing the cropping pattern. Moreover, the classification accuracy, which was as high as 85.02% in terms of overall accuracy, with a kappa coefficient of 0.824, from multi-temporal ALOS-2/PALSAR-2 data, was obtained. These results show that multi-temporal ALOS-2/PALSAR-2 data are capable of discriminating between two different paddy field cropping types, as well as beneficial for discriminating between the cropping stage and cropping pattern information for several other land uses.

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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.

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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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19

Tuia, Devis, Diego Marcos, and Gustau Camps-Valls. "Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization." ISPRS Journal of Photogrammetry and Remote Sensing 120 (October 2016): 1–12. http://dx.doi.org/10.1016/j.isprsjprs.2016.07.004.

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20

Liu, Wu Ping, and Fu Wei. "Using Local Transition Probability Models in Markov Random Field for Multi-Temporal Image Classification." Applied Mechanics and Materials 687-691 (November 2014): 3963–67. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3963.

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Making use full of multi-source and multi-temporal information to extract richer and interesting information is a tendency in analysis of remote sensing images. In this paper, spatial and temporal contextual classification based on Markov Random Field (MRF) is used to classify ecological function vegetation in Poyang Lake. The results show that spatial and temporal neighborhood complementary information from different images can be used to remove the spectral confusion of different kinds of vegetation on single image and improve classification accuracy compared to MLC method. Building effective spatial and temporal neighborhood model for information extraction in special application is the key of multi-source and multi-temporal image analysis. Although spatial and temporal contextual classification method is computation demanding, it’s promising in the application emphasizing classification accuracy.

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21

Gao, G., M. Zhang, and Y. Gu. "OBJECT MANIFOLD ALIGNMENT FOR MULTI-TEMPORAL HIGH RESOLUTION REMOTE SENSING IMAGES CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 325–32. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-325-2017.

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Multi-temporal remote sensing images classification is very useful for monitoring the land cover changes. Traditional approaches in this field mainly face to limited labelled samples and spectral drift of image information. With spatial resolution improvement, “pepper and salt” appears and classification results will be effected when the pixelwise classification algorithms are applied to high-resolution satellite images, in which the spatial relationship among the pixels is ignored. For classifying the multi-temporal high resolution images with limited labelled samples, spectral drift and “pepper and salt” problem, an object-based manifold alignment method is proposed. Firstly, multi-temporal multispectral images are cut to superpixels by simple linear iterative clustering (SLIC) respectively. Secondly, some features obtained from superpixels are formed as vector. Thirdly, a majority voting manifold alignment method aiming at solving high resolution problem is proposed and mapping the vector data to alignment space. At last, all the data in the alignment space are classified by using KNN method. Multi-temporal images from different areas or the same area are both considered in this paper. In the experiments, 2 groups of multi-temporal HR images collected by China GF1 and GF2 satellites are used for performance evaluation. Experimental results indicate that the proposed method not only has significantly outperforms than traditional domain adaptation methods in classification accuracy, but also effectively overcome the problem of “pepper and salt”.

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22

Ye, Peng. "Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges." International Journal of Environmental Research and Public Health 19, no. 6 (March 20, 2022): 3701. http://dx.doi.org/10.3390/ijerph19063701.

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Meteorological disaster monitoring is an important research direction in remote sensing technology in the field of meteorology, which can serve many meteorological disaster management tasks. The key issues in the remote sensing monitoring of meteorological disasters are monitoring task arrangement and organization, meteorological disaster information extraction, and multi-temporal disaster information change detection. To accurately represent the monitoring tasks, it is necessary to determine the timescale, perform sensor planning, and construct a representation model to monitor information. On this basis, the meteorological disaster information is extracted by remote sensing data-processing approaches. Furthermore, the multi-temporal meteorological disaster information is compared to detect the evolution of meteorological disasters. Due to the highly dynamic nature of meteorological disasters, the process characteristics of meteorological disasters monitoring have attracted more attention. Although many remote sensing approaches were successfully used for meteorological disaster monitoring, there are still gaps in process monitoring. In future, research on sensor planning, information representation models, multi-source data fusion, etc., will provide an important basis and direction to promote meteorological disaster process monitoring. The process monitoring strategy will further promote the discovery of correlations and impact mechanisms in the evolution of meteorological disasters.

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Kowsalya, G., and T. K. Revathi. "Detection of Unsupervised Changes in a Multi-temporal remote sensing Image." i-manager's Journal on Electronics Engineering 3, no. 4 (August 15, 2013): 25–33. http://dx.doi.org/10.26634/jele.3.4.2395.

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24

Zhong, J., and R. Wang. "Multi‐temporal remote sensing change detection based on independent component analysis." International Journal of Remote Sensing 27, no. 10 (May 2006): 2055–61. http://dx.doi.org/10.1080/01431160500444756.

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25

Zheng, Baojuan, James B. Campbell, and Kirsten M. de Beurs. "Remote sensing of crop residue cover using multi-temporal Landsat imagery." Remote Sensing of Environment 117 (February 2012): 177–83. http://dx.doi.org/10.1016/j.rse.2011.09.016.

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26

Xiaoyu, Song, Cui Bei, Yang Guijun, and Feng Haikuan. "Comparison of winter wheat growth with multi-temporal remote sensing imagery." IOP Conference Series: Earth and Environmental Science 17 (March 18, 2014): 012044. http://dx.doi.org/10.1088/1755-1315/17/1/012044.

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Li, Xun, Wendy Y. Chen, Giovanni Sanesi, and Raffaele Lafortezza. "Remote Sensing in Urban Forestry: Recent Applications and Future Directions." Remote Sensing 11, no. 10 (May 14, 2019): 1144. http://dx.doi.org/10.3390/rs11101144.

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Increasing recognition of the importance of urban forest ecosystem services calls for the sustainable management of urban forests, which requires timely and accurate information on the status, trends and interactions between socioeconomic and ecological processes pertaining to urban forests. In this regard, remote sensing, especially with its recent advances in sensors and data processing methods, has emerged as a premier and useful observational and analytical tool. This study summarises recent remote sensing applications in urban forestry from the perspective of three distinctive themes: multi-source, multi-temporal and multi-scale inputs. It reviews how different sources of remotely sensed data offer a fast, replicable and scalable way to quantify urban forest dynamics at varying spatiotemporal scales on a case-by-case basis. Combined optical imagery and LiDAR data results as the most promising among multi-source inputs; in addition, future efforts should focus on enhancing data processing efficiency. For long-term multi-temporal inputs, in the event satellite imagery is the only available data source, future work should improve haze-/cloud-removal techniques for enhancing image quality. Current attention given to multi-scale inputs remains limited; hence, future studies should be more aware of scale effects and cautiously draw conclusions.

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Yang, Bin, Wanxue Zhu, Ehsan Eyshi Rezaei, Jing Li, Zhigang Sun, and Junqiang Zhang. "The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing." Remote Sensing 14, no. 7 (March 24, 2022): 1559. http://dx.doi.org/10.3390/rs14071559.

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Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (R2 increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (R2 = 0.93, RMSE = 0.77 t·ha−1). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction.

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Meshkini, K., F. Bovolo, and L. Bruzzone. "A 3D CNN APPROACH FOR CHANGE DETECTION IN HR SATELLITE IMAGE TIME SERIES BASED ON A PRETRAINED 2D CNN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 143–50. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-143-2022.

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Abstract. Over recent decades, Change Detection (CD) has been intensively investigated due to the availability of High Resolution (HR) multi-spectral multi-temporal remote sensing images. Deep Learning (DL) based methods such as Convolutional Neural Network (CNN) have recently received increasing attention in CD problems demonstrating high potential. However, most of the CNN-based CD methods are designed for bi-temporal image analysis. Here, we propose a Three-Dimensional (3D) CNN-based CD approach that can effectively deal with HR image time series and process spatial-spectral-temporal features. The method is unsupervised and thus does not require the complex task of collecting labelled multi-temporal data. Since there are only a few pretrained 3D CNNs available that are not suitable for remote sensing CD analysis, the proposed approach starts with a pretrained 2D CNN architecture trained on remote sensing images for semantic segmentation and develops a 3D CNN architecture using a transfer learning technique to jointly deal with spatial, spectral and temporal information. A layerwise feature reduction strategy is performed to select the most informative features and a pixelwise year-based Change Vector Analysis (CVA) is employed to identify changed pixels. Experimental results on a long time series of Landsat 8 images for an area located in Saudi Arabia confirm the effectiveness of the proposed approach.

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Ma, Guangdi, and Weichen Yang. "Dynamic reconstruction method of unmanned aerial vehicle aerial remote sensing image based on compressed sensing." Journal of Geography and Cartography 5, no. 1 (December 5, 2021): 17. http://dx.doi.org/10.24294/jgc.v5i1.1413.

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Aiming at the current problems of poor dynamic reconstruction of UAV aerial remote sensing images and low image clarity, the dynamic reconstruction method of UAV aerial remote sensing images based on compression perception is proposed. Construct a quality reduction model for UAV aerial remote sensing images, obtain image feature information, and further noise reduction preprocessing of UAV aerial remote sensing images to better improve the resolution, spectral and multi-temporal trends of UAV aerial remote sensing images, and effectively solve the problems of resource waste such as large amount of sampled data, long sampling time and large amount of data transmission and storage. Maximize the UAV aerial remote sensing images sampling rate, reduce the complexity of dynamic reconstruction of UAV aerial remote sensing images, and effectively obtain the research requirements of high-quality image reconstruction. The experimental results show that the proposed dynamic reconstruction method of UAV aerial remote sensing images based on compressed sensing is correct and effective, which is better than the current mainstream methods.

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31

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.

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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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32

Ebel, P., S. Saha, and X. X. Zhu. "FUSING MULTI-MODAL DATA FOR SUPERVISED CHANGE DETECTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 243–49. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-243-2021.

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Abstract. With the rapid development of remote sensing technology in the last decade, different modalities of remote sensing data recorded via a variety of sensors are now easily accessible. Different sensors often provide complementary information and thus a more detailed and accurate Earth observation is possible by integrating their joint information. While change detection methods have been traditionally proposed for homogeneous data, combining multi-sensor multi-temporal data with different characteristics and resolution may provide a more robust interpretation of spatio-temporal evolution. However, integration of multi-temporal information from disparate sensory sources is challenging. Moreover, research in this direction is often hindered by a lack of available multi-modal data sets. To resolve these current shortcomings we curate a novel data set for multi-modal change detection. We further propose a novel Siamese architecture for fusion of SAR and optical observations for multi-modal change detection, which underlines the value of our newly gathered data. An experimental validation on the aforementioned data set demonstrates the potentials of the proposed model, which outperforms common mono-modal methods compared against.

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33

Liu, Zhi Hong, Xing Ke Yang, Qian Zhu, Hu Jun He, and San You Cheng. "Study on Macroscopically Dynamic Monitoring of Newly Increased Construction Land in Northwestern Plains Based on Middle Resolution Remote Sensing Data." Applied Mechanics and Materials 333-335 (July 2013): 1475–78. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1475.

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Analyzing the significance of macroscopically dynamic monitoring of newly increased construction land, and considering the influence of various factors, this paper selects central Shaanxi Plain in Northwestern region for a typical experimental zone, setting up knowledge base of remote sensing images interpretation, using multi-temporal remote sensing images, carrying through interactive interpretation of change patterns spots of newly increased construction land and field validation. Results of middle resolution remote sensing image interpretation are compared, analyzed. Additionally, interpretation accuracy of different scales are studied, especially between middle resolution 10 ms ALOS remote sensing image and panchromatic high resolution remote sensing, on newly increased construction land in northwestern plains, to find out the remote sensing images which can not only quickly extract new construction land change patterns spots, but also can satisfy precision requirement of the business.

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34

Averkiev, N. F., A. V. Kulvits, and T. A. Zhitnikov. "MULTILEVEL BALLISTIC STRUCTURE OF THE CLUSTER ORBITAL GROUPING OF REMOTE SENSING OF THE EARTH." Izvestiya of Samara Scientific Center of the Russian Academy of Sciences 23, no. 4 (2021): 77–85. http://dx.doi.org/10.37313/1990-5378-2021-23-4-77-85.

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The features of the application and justification of the orbital groupings of remote sensing of the Earth, consisting of clusters of small spacecraft, are considered. A review and analysis of the ballistic justification, construction and features of the use of orbital groups of remote sensing of the Earth is carried out. Modern approaches to the ballistic design of periodic review orbital groupings are considered. The article considers a new integrated approach to the ballistic construction of promising cluster orbital groupings, which will allow providing the spatio-temporal and accuracy characteristics required by the consumer, due to the optimal multi-level ballistic structure. The fundamental principles of constructing a cluster orbital grouping with a multi-level ballistic structure are formulated. The stages of the formation of a multi-level ballistic structure are considered in detail, from the standpoint of a systematic approach. A mathematical formulation of the problem and a hierarchy of performance indicators are proposed. For a meaningful description of the simulated system, a conceptual model for substantiating multi-level ballistic structures of a cluster orbital grouping of remote sensing of the Earth under the influence of the external environment has been developed. The model shows the interrelationships of the main elements of the substantiation of the ballistic structure of the cluster orbital grouping of remote sensing of the Earth and the sequence of formation of particular problems. The results of modeling both the ballistic structure of the cluster and the ballistic structure of the Earth remote sensing orbital grouping, which provides a set of tactical and technical, spatio-temporal and structurally stable consumer requirements, are presented. The effect of the application of the developed conceptual model will be the optimal strategy for the use of cluster orbital groupings of remote sensing of the Earth, which will provide the required value of its effectiveness under the influence of the external environment.

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35

Zhang, Xinyue, Chengcai Leng, Yameng Hong, Zhao Pei, Irene Cheng, and Anup Basu. "Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey." Remote Sensing 13, no. 24 (December 17, 2021): 5128. http://dx.doi.org/10.3390/rs13245128.

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With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations.

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36

Plotnikov, D. E., P. A. Kolbudaev, and S. A. Bartalev. "Identification of dynamically homogeneous areas with time series segmentation of remote sensing data." Computer Optics 42, no. 3 (July 25, 2018): 447–56. http://dx.doi.org/10.18287/2412-6179-2018-42-3-447-456.

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We propose a method of segmentation of remote sensing time series data, which exploits multi-temporal information to identify objects’ boundaries. Extracting homogeneous objects with similar temporal behavior, the method analyzes large volumes of multi-temporal input data in a piecewise way and produces a consistent output segmentation layer for large territories. Segment building logic is simplified to minimize the computation time, while objects’ boundary identification accuracy remains sufficient for remote monitoring and mapping of vegetation, and specifically, agricultural crops. At the Space Research Institute of the RAS, the proposed method is currently applied for automated on-line satellite imagery analysis for recognition and mapping of (winter and spring) crops on large territories and land-use evaluation. The method successfully deals with gaps in remote sensing time series data and performs well even when input images are contaminated with speckle noise. Due to its ability to map dynamically homogeneous surface areas with partially missing data, the method provides a potential for their recovery.

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37

Butenuth, Matthias, Daniel Frey, Allan Aasbjerg Nielsen, and Henning Skriver. "Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery." International Journal of Remote Sensing 32, no. 23 (September 28, 2011): 8575–94. http://dx.doi.org/10.1080/01431161.2010.542204.

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38

Qu, T., Q. Xu, C. Liu, Z. Li, B. Chen, and K. Dai. "RADAR REMOTE SENSING APPLICATIONS IN LANDSLIDE MONITORING WITH MULTI-PLATFORM INSAR OBSERVATIONS: A CASE STUDY FROM CHINA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1939–43. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1939-2019.

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Abstract. In China, landslides are widely distributed in the mountainous areas of western regions. Especially after the Wenchuan Earthquake in 2008, a large number of landslides were triggered. This work focuses on the deformation monitoring of Xishancun Landslide based on multi-platform spaceborne radar remote sensing techniques. The spatio-temporal deformation characteristics of landslide could be retrieved from time series InSAR processing with joint use of Sentinel-1 and TerraSAR-X datasets. Eventually, the deformation and evolution histories are cultivated thoroughly to realize an effective and comprehensive monitoring and research of Xishancun Landslide. This work concludes that spaceborne radar remote sensing applications could demonstrate great potentials to identify the spatio-temporal characteristics and investigate the kinematics for hazardous landslides, especially combined with in situ measurements and other remote sensing observations.

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39

Zhang, Yuqi, Wei Li, Yaohua Wang, Zhibin Wang, and Hao Li. "Beyond Classifiers: Remote Sensing Change Detection with Metric Learning." Remote Sensing 14, no. 18 (September 8, 2022): 4478. http://dx.doi.org/10.3390/rs14184478.

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For change detection in remote sensing images, supervised learning always relies on bi-temporal images as inputs and 2-class/multi-class classifiers as outputs. On the other hand, change detection can be viewed as a metric learning problem, i.e., changed areas should be dissimilar while unchanged areas should be similar. In this paper, we study several metric learning formulations for change detection. A strong baseline is achieved by training on pair-wise images with Reverted Contrastive Loss (RCL) with hard mining. Motivated by the success of triplet loss, we seek two sources of triplet pairs from the bi-temporal images, and a novel Spatial–Temporal Triplet Loss (STTL) is proposed. The proposed triplet loss is further validated on semantic change detection, where semantic labels are provided for the changed areas. The experimental results prove state-of-the-art performance on both binary and semantic change detection.

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40

Shew, A. M., and A. Ghosh. "USING MULTI-TEMPORAL REMOTE SENSING DATA TO ANALYZE THE SPATIO-TEMPORAL PATTERNS OF DRY SEASON RICE PRODUCTION IN BANGLADESH." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 19, 2017): 61–68. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-61-2017.

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Remote sensing in the optical domain is widely used in agricultural monitoring; however, such initiatives pose a challenge for developing countries due to a lack of high quality in situ information. Our proposed methodology could help developing countries bridge this gap by demonstrating the potential to quantify patterns of dry season rice production in Bangladesh. To analyze approximately 90,000 km2 of cultivated land in Bangladesh at 30 m spatial resolution, we used two decades of remote sensing data from the Landsat archive and Google Earth Engine (GEE), a cloud-based geospatial data analysis platform built on Google infrastructure and capable of processing petabyte-scale remote sensing data. We reconstructed the seasonal patterns of vegetation indices (VIs) for each pixel using a harmonic time series (HTS) model, which minimizes the effects of missing observations and noise. Next, we combined the seasonality information of VIs with our knowledge of rice cultivation systems in Bangladesh to delineate rice areas in the dry season, which are predominantly hybrid and High Yielding Varieties (HYV). Based on historical Landsat imagery, the harmonic time series of vegetation indices (HTS-VIs) model estimated 4.605 million ha, 3.519 million ha, and 4.021 million ha of rice production for Bangladesh in 2005, 2010, and 2015 respectively. Fine spatial scale information on HYV rice over the last 20 years will greatly improve our understanding of double-cropped rice systems, current status of production, and potential for HYV rice adoption in Bangladesh during the dry season.

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41

Li, Hanyu, Hermann Kaufmann, and Guochang Xu. "Modeling Spatio-temporal Drought Events Based on Multi-temporal, Multi-source Remote Sensing Data Calibrated by Soil Humidity." Chinese Geographical Science 32, no. 1 (November 6, 2021): 127–41. http://dx.doi.org/10.1007/s11769-021-1250-4.

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42

Censi, Alessandro Michele, Dino Ienco, Yawogan Jean Eudes Gbodjo, Ruggero Gaetano Pensa, Roberto Interdonato, and Raffaele Gaetano. "Attentive Spatial Temporal Graph CNN for Land Cover Mapping From Multi Temporal Remote Sensing Data." IEEE Access 9 (2021): 23070–82. http://dx.doi.org/10.1109/access.2021.3055554.

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43

Sanches, I. D., R. Q. Feitosa, P. Achanccaray, B. Montibeller, A. J. B. Luiz, M. D. Soares, V. H. R. Prudente, D. C. Vieira, and L. E. P. Maurano. "LEM BENCHMARK DATABASE FOR TROPICAL AGRICULTURAL REMOTE SENSING APPLICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 387–92. http://dx.doi.org/10.5194/isprs-archives-xlii-1-387-2018.

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Abstract. The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data.

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Zhao, H., H. Wang, W. Wu, and C. Wang. "Integrated 3S Technology Used in Urban Grid Management." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2 (November 11, 2014): 1–5. http://dx.doi.org/10.5194/isprsarchives-xl-2-1-2014.

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Sustainable development requires monitoring the state and changes of the city and providing the appropriate information to users anytime and anywhere. This paper takes Ningbo City as the research area, by utilizing two temporal (March 25, 2012 and November 25, 2013) ZY-3 satellite remote sensing data. 3S technology is used for urban grid management. The remote sensing information extraction of Ningbo City includes: extraction of building change information, extraction of green space change information, extraction of water information and analysis of eutrophication correspondingly. When extracting change information, we take "change information" as a special kind of "geographic information" to study the characteristics of different bands in multi-temporal data, and follow the first law of geography, namely adjacent similar principle. The extracted raster information is further converted into GIS vector format data as a basis for dynamic monitoring of Ningbo Urban Management systems: on the one hand, it can meet the demands of multi-source spatial data analysis (such as: overlay analysis, buffer analysis, etc.); on the other hand, it could meet the requirements of daily urban management. Dynamic monitoring system of Ningbo city management adopts the urban grid management mode. Based on GIS and GPS, grid management can satisfy the urban management mode –someone bear responsibility within the grid, somebody do the task under the supervision of the lattice, and at the same time play the role of remote sensing field surveying. To some extent, integrated 3S technology and urban grid management is a practical alternative of minimizing the uncertainty of remote sensing data and information extraction. With multi-scale and multi-dimensional remote sensing data, 3S integration and the urban grid management can monitor the urban state and its spatial-temporal changes. It’s helpful for discovery and analysis of urban problems about resources, environment, ecology and disaster, from phenomena to nature, and it is a necessary part of sustainable urbanization.

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Li, Weisheng, Dongwen Cao, and Minghao Xiang. "Enhanced Multi-Stream Remote Sensing Spatiotemporal Fusion Network Based on Transformer and Dilated Convolution." Remote Sensing 14, no. 18 (September 11, 2022): 4544. http://dx.doi.org/10.3390/rs14184544.

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Remote sensing images with high temporal and spatial resolutions play a crucial role in land surface-change monitoring, vegetation monitoring, and natural disaster mapping. However, existing technical conditions and cost constraints make it very difficult to directly obtain remote sensing images with high temporal and spatial resolution. Consequently, spatiotemporal fusion technology for remote sensing images has attracted considerable attention. In recent years, deep learning-based fusion methods have been developed. In this study, to improve the accuracy and robustness of deep learning models and better extract the spatiotemporal information of remote sensing images, the existing multi-stream remote sensing spatiotemporal fusion network MSNet is improved using dilated convolution and an improved transformer encoder to develop an enhanced version called EMSNet. Dilated convolution is used to extract time information and reduce parameters. The improved transformer encoder is improved to further adapt to image-fusion technology and effectively extract spatiotemporal information. A new weight strategy is used for fusion that substantially improves the prediction accuracy of the model, image quality, and fusion effect. The superiority of the proposed approach is confirmed by comparing it with six representative spatiotemporal fusion algorithms on three disparate datasets. Compared with MSNet, EMSNet improved SSIM by 15.3% on the CIA dataset, ERGAS by 92.1% on the LGC dataset, and RMSE by 92.9% on the AHB dataset.

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Onojeghuo, Alex, and Ajoke Onojeghuo. "Protected Area Monitoring in the Niger Delta Using Multi-Temporal Remote Sensing." Environments 2, no. 4 (October 26, 2015): 500–520. http://dx.doi.org/10.3390/environments2040500.

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47

Lal, Anisha M., and S. Margret Anouncia. "Enhanced Dictionary based Sparse Representation Fusion for Multi-temporal Remote Sensing Images." European Journal of Remote Sensing 49, no. 1 (January 2016): 317–36. http://dx.doi.org/10.5721/eujrs20164918.

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48

Raab, Christoph, H. G. Stroh, B. Tonn, M. Meißner, N. Rohwer, N. Balkenhol, and J. Isselstein. "Mapping semi-natural grassland communities using multi-temporal RapidEye remote sensing data." International Journal of Remote Sensing 39, no. 17 (August 3, 2018): 5638–59. http://dx.doi.org/10.1080/01431161.2018.1504344.

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49

Geng, Xiurui, Luyan Ji, and Yongchao Zhao. "Filter tensor analysis: A tool for multi-temporal remote sensing target detection." ISPRS Journal of Photogrammetry and Remote Sensing 151 (May 2019): 290–301. http://dx.doi.org/10.1016/j.isprsjprs.2019.03.008.

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50

Du, Peijun, Sicong Liu, Junshi Xia, and Yindi Zhao. "Information fusion techniques for change detection from multi-temporal remote sensing images." Information Fusion 14, no. 1 (January 2013): 19–27. http://dx.doi.org/10.1016/j.inffus.2012.05.003.

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