Remote sensing image change detection based on stacked denoising auto-encoder neural network
In order to meet the urgent need for automatic discovery of land type changes in natural resource survey and monitoring in Guangdong Province, a stacked denoising auto-encoder (SDAE) deep neural network was used to extract semantic information from high-resolution remote sensing images. One scene of BJ2, GF1, GF2, GJ1, ZY3, SP6, and PL0 satellite images in the Pearl River Delta experimental area was selected. The sample data was classified according to resolution, and the multi-sensor training models were trained and generated separately. While identifying the target area, the false alarm rate of the changing area was reduced, achieving the extraction of change information from images in two periods. In addition, change confidence and change type were output. The experiment shows that the recall rates of this model in the urban-rural fringe areas, built-up areas, and rural areas of the Pearl River Delta are 90％, 83％, and 82％, respectively, and the accuracy rates are 61％, 60％, and 71％, respectively, demonstrating that the method is feasible.
万方数据
维普
