Deep learning based remote sensing technique for environmental parameter retrieval and data fusion from physical models

Vemuri, Ratna Kumari; Reddy, Pundru Chandra Shaker; Kumar, Bhavesh Shri; Ravi, J; Sharma, Sudhir; Ponnusamy, Sivakumar

doi:10.1007/s12517-021-07577-3

Cited by 26 publications

(11 citation statements)

References 16 publications

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“…This article uses remote sensing images provided by Landsat-8 satellite as the data source and analyses the environmental situation of Nansi Lake through a combination of three methods [3][4][5][6].…”

Section: Data and Used Methodsmentioning

confidence: 99%

Analysis of Spatio-temporal Change Characteristics of Water Environment in Nansi Lake Based on Satellite Remote Sensing Monitoring

Wang,

Yu,

Wang

et al. 2023

SHS Web of Conf.

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Lakes play a crucial role in the preservation and distribution of surface water resources, making them integral components of environmental protection. This study utilizes remote sensing data obtained from Landsat-8 to investigate the temporal changes in water area, land use patterns, and distribution of cyanobacteria in Nansi Lake between 2015 and 2023. Furthermore, it conducts an extensive analysis of the environmental condition of Nansi Lake, considering the escalating environmental crises resulting from industrial and agricultural development in recent decades. By adopting a multidimensional approach, this article offers a comprehensive examination of the environmental situation in the Nansi Lake region. Notably, the findings of this study exhibit enhanced accuracy and practicality when compared to conventional environmental analysis methods. Consequently, this research contributes novel ideas and methodologies for the monitoring and safeguarding of the ecological environment of lakes through the application of remote sensing techniques.

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Section: Data and Used Methodsmentioning

confidence: 99%

Analysis of Spatio-temporal Change Characteristics of Water Environment in Nansi Lake Based on Satellite Remote Sensing Monitoring

Wang,

Yu,

Wang

et al. 2023

SHS Web of Conf.

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“…Despite the preliminary data processing, however, the amount of data received by the coordinator for each data acquisition is still very large, including the environmental parameters transmitted from the acquisition nodes [22]. D-S evidence theory is used to realize the fusion analysis of various environmental parameters, and the support degree of each set of data to various hypotheses is given to guide the control decision.…”

Section: Image Fusion D-s Evidence Theory Fusion Methods Ismentioning

confidence: 99%

High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing

Duan

2021

Scientific Programming

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Existing remote sensing data classification methods cannot achieve the sharing of remote sensing image spectrum, leading to poor fusion and classification of remote sensing data. Therefore, a high spatial resolution remote sensing data classification method based on spectrum sharing is proposed. A page frame recovery algorithm (PFRA) is introduced to allocate the wireless spectrum resources in low-frequency band, and a dynamic spectrum sharing mechanism is designed between the primary and secondary users of remote sensing images. Based on this, D-S evidence theory is used to fuse high spatial resolution remote sensing data and correct the pixel brightness of the fused multispectral image. The initial data are normalized, the feature of spectral image is extracted, the convolution neural network classification model is constructed, and the remote sensing image is segmented. Experimental results show that the proposed method takes shorter time and has higher accuracy for high spatial resolution image segmentation. High spatial resolution remote sensing data classification is more efficient, and the accuracy of data classification and remote sensing image fusion are more ideal.

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“…After years of changes, these impacts will leave obvious characteristics on remote sensing images. Therefore, remote sensing technology can be used to visually view the ecological environment during different periods, so as to get the specific situation of environmental pollution [1] [2] . Moreover, traditional water pollution monitoring generally adopts the ground fixed-point sampling analysis method.…”

Section: Introductionmentioning

confidence: 99%

Water pollution environment information extraction based on Landsat images

Wen

Wang

2023

International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022)

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Taihu Lake, one of the five largest freshwater lakes in China, is located in the south of the Yangtze River Delta, with a water area of 2338.1km2. In recent years, with the deterioration of water quality, the water pollution in Taihu Lake has become increasingly serious. In 2007, a large-scale outbreak of cyanobacteria caused a water crisis for nearly 2 million people in Wuxi. The main performance of water pollution is that the content of nitrogen and ammonia in water exceeds the standard, which leads to eutrophication of water and excessive proliferation of algae. Therefore, this paper uses Landsat-5 TM, Landsat-7 ETM and Landsat-8 OLI remote sensing data to study the information extraction methods of eutrophic polluted water bodies in Taihu region. By comparing with the health status report on Taihu Lake, it is found that the calculation results of remote sensing data can well reflect the distribution characteristics of ammonia nitrogen pollution in this area, and provide a basis for monitoring and controlling ammonia nitrogen pollution in this area by using remote sensing technology.

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Deep learning based remote sensing technique for environmental parameter retrieval and data fusion from physical models

Cited by 26 publications

References 16 publications

Analysis of Spatio-temporal Change Characteristics of Water Environment in Nansi Lake Based on Satellite Remote Sensing Monitoring

Analysis of Spatio-temporal Change Characteristics of Water Environment in Nansi Lake Based on Satellite Remote Sensing Monitoring

High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing

Water pollution environment information extraction based on Landsat images

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