In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the...In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm (GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network) and classification based on GIS and knowledge are proposed.展开更多
The subsiding land can be extracted from Remote Sensing image based on itsspectral and spatial features. The features of subsiding land caused by raining, especially its RSinformation features and relative knowledge a...The subsiding land can be extracted from Remote Sensing image based on itsspectral and spatial features. The features of subsiding land caused by raining, especially its RSinformation features and relative knowledge are proposed. Three methods can be used to extractsubsiding land from RS image. The first is to categorize the region into different blocks (orlayers) according to their features and apply corresponding strategies for each block, the second isto identify the changeable region based on GIS firstly and then to classify those regions, and thethird is to post-process the classified image by traditional methods or ANN (Artificial NeuralNetwork) methods based on domain knowledge and GIS. Two direct extraction methods are introducedalso. One is the extraction based on the water accumulating property of subsiding land, and theother is based on the dynamic change of land cover in subsiding land.展开更多
基金Under the auspices of the Research Foundation of Doctoral Point of China(No.RFDP20010290006).
文摘In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm (GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network) and classification based on GIS and knowledge are proposed.
基金Under the auspices of the Research Foundation of Doctoral Point of China(No.RFDP20010290006).
文摘The subsiding land can be extracted from Remote Sensing image based on itsspectral and spatial features. The features of subsiding land caused by raining, especially its RSinformation features and relative knowledge are proposed. Three methods can be used to extractsubsiding land from RS image. The first is to categorize the region into different blocks (orlayers) according to their features and apply corresponding strategies for each block, the second isto identify the changeable region based on GIS firstly and then to classify those regions, and thethird is to post-process the classified image by traditional methods or ANN (Artificial NeuralNetwork) methods based on domain knowledge and GIS. Two direct extraction methods are introducedalso. One is the extraction based on the water accumulating property of subsiding land, and theother is based on the dynamic change of land cover in subsiding land.
