Application of Isolated Forest Algorithm in Deep Learning Change Detection of High Resolution Remote Sensing Image

This paper proposes a deep learning change detection method that uses the isolated forest algorithm to optimize the change detection results. Use the improved change vector analysis algorithm and gray level co-occurrence matrix algorithm to obtain the image spectrum and texture difference characteristics, select samples and train the deep confidence network model to detect the image change area; introduce the isolated forest algorithm to optimize the model detection result and get the change detection map. In the experiments based on the WHU Building Dataset, the accuracy and recall of the deep learning change detection results optimized by the method improved by 22.83% and 2.79%, respectively, and the false alarm rate and missed detection rate decreased by 36.88% and 2.79%, indicating that this article The method can effectively improve the accuracy of deep learning change detection, and has certain generalization value.