Object-wise joint-classification change detection for remote sensing images based on entropy query-by fuzzy ARTMAP

Abstract

The pixel-wise post-classification comparison (PCC) method is widely used in remote sensing images change detection. However, it is affected by the significant cumulative error caused by single image classification error. What’s more, the pixel-wise change detection method always produces “salt and pepper” effect. To solve the excessive evaluation of changed types and quantity caused by cumulative error and “salt and pepper” effect, a novel remote sensing image change detection method called entropy query-by fuzzy ARTMAP object-wise joint classification comparison (EQFAM-OBJCC) is presented in this article. Firstly, entropy query-by measurement of active learning is integrated with the fuzzy ARTMAP neural network to choose training samples which contain large amounts of information to improve the classification accuracy. Secondly, joint classification comparison is introduced to obtain the pixel-wise classification results. Finally, the object-wise classification and change detection results are produced by superpixel segmentation method, majority voting rule, and comparison of each superpixels. Experimental results demonstrate the validity of the proposed method. The classification and change detection results show that the proposed method can reduce the cumulative error with an average classification accuracy of 94.12% and a total detection error of 27.03%, and effectively resolve the “salt and pepper” problem. The proposed method was used to monitor the reclamation status of Liaohe estuary wetland via 10 time series remote sensing images from 1987 to 2014.