A Novel Hyperspectral Image Change Detection Framework Based on 3D-Wavelet Domain Active Convolutional Neural Network

Abstract

Change detection techniques of hyperspectral images(HSI) has witnessed great improvements with the applications of deep convolutional networks (CNN). In this paper, we propose a novel HSI change detection framework based on 3D-Wavelet domain active convolutional neural network. First, the bi-temporal hyperspectral difference image is decomposed into directional subbands by the discrete 3D-Wavelet transform, which is capable of suppressing the noise information of the HSIs. Then, to enhance the change discrimination ability of the primary features, the directional subbands are concatenated from coarse to fine scales to form the initial 3D-Wavelet feature map. In the conventional implementation, active learning strategy is iteratively adopted to extract the deep features of the constructed feature map, in each active learning round, the most informative unlabeled samples will be selected to enlarge the training set, which greatly reduces the labor of annoteing data. Results on two realworld hyperspectral change detection datasets demonstrates the effectiveness of the proposed approach.