Land Use/Cover Change Detection Based on Artificial Neural Network

ANN has been introduced in land use/cover change detection to improve the change detection results.In this paper,the input and output,the structure and reasonable settings of ANN have been studied and compared.Different ANN models and algorithms have been introduced to improve the performance of ANN.The results have shown that LVQ and MAALR(momentum-adaptive adjust ment of learning rate) have turned to be more efficient in land use/cover change detection than BPNN because they take less ANN training time and have no local minimum. The experiments based on TM satellite images of different time have shown that ANN method is practical and efficient for the change detection.The accuracy of it is higher than those of the traditional methods,and it can provide both changed areas and categories at the same time.Besides,it is easy to integrate multi-source data because of low demand for data distribution.