Urban Land Use Change Detection Based on High Accuracy Classification of Multispectral Remote Sensing Imagery

In the present paper, the urban land change in Jiading district of Shanghai was studied on the basis of high accuracy classification for 4 epochs of multispectral remotely sensed imageries. A further improved genetic-algorithm optimized back propagation neural network approach was first employed in our study to obtain sorts of land cover types from the remotely sensed imageries. The urban land and non-urban land types were thus extracted based on the classification result. According to the 16 corresponding relationships between the pixel values in the four urban land imageries and the ones in the generated urban land change imagery, the amount of each type pixel in the generated imagery was calculated according to the four plates, and the situation of urban land change was analyzed and investigated for the study area in three year intervals. The urban development in the study area was also preliminarily revealed.