A new method of target change detection based on network in network structure

A new method based on a Network in Network (NIN) structure is proposed to detect target changes from multi-temporal optical remote sensing images. Firstly, the changed areas are captured by a change detection method based on multifeature fusion, and the changed patches are obtained by morphological processing. Then, a convolutional neural network with an NIN structure is constructed to train the target recognition model using a small number of samples and to distinguish the original images corresponding to the tchanged patches. Finally, a recognition strategy combining preliminary screening and thorough screening is designed, and multiple thresholds are assigned according to the patch size to avoid the possible false detection brought by a single threshold. Based on experiments with multi-temporal airport images, the overall accuracy of aircraft target change detection using the method in this study was 91.89%, with a false alarm rate of 10.71%, indicating that this method can accurately and reliably detect target change.