Imbalanced Learning-based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net

TL;DR

This paper introduces a novel imbalanced learning approach using a supervised PCA-Net for SAR image change detection, effectively handling class imbalance and boundary information to improve accuracy over traditional methods.

Contribution

It proposes a morphologically supervised PCA-Net that leverages boundary pixel knowledge to enhance feature extraction and change detection in imbalanced SAR datasets.

Findings

Outperforms traditional difference map methods.

Effectively utilizes boundary pixel information.

Demonstrates robustness across multiple SAR datasets.

Abstract

Change detection is a quite challenging task due to the imbalance between unchanged and changed class. In addition, the traditional difference map generated by log-ratio is subject to the speckle, which will reduce the accuracy. In this letter, an imbalanced learning-based change detection is proposed based on PCA network (PCA-Net), where a supervised PCA-Net is designed to obtain the robust features directly from given multitemporal SAR images instead of a difference map. Furthermore, to tackle with the imbalance between changed and unchanged classes, we propose a morphologically supervised learning method, where the knowledge in the pixels near the boundary between two classes are exploited to guide network training. Finally, our proposed PCA-Net can be trained by the datasets with available reference maps and applied to a new dataset, which is quite practical in change detection projects. Our proposed method is verified on five sets of multiple temporal SAR images. It is demonstrated from the experiment results that with the knowledge in training samples from the boundary, the learned features benefit for change detection and make the proposed method outperforms than supervised methods trained by randomly drawing samples.