Mask DeepLab: End-to-end image segmentation for change detection in high-resolution remote sensing images

Abstract

Traditional change detection (CD) algorithms cannot meet the requirements of today’s high resolution remote sensing images (HR). Recently, deep learning-based CD has become a popular research topic. However, there are not many annotated samples for training deep learning (DL) models. Patch-based algorithm has become an important research direction in CD in response to the lack of training datasets, but the optimal patch size is relatively small and difficult to determine, which limits the use of spatial information and the extension of deep network. In this paper, we develop a feature-regularized mask DeepLab (FRM-DeepLab) for HRCD. First, a mask-based framework (MaskNet) that uses a few annotated samples to update model parameters is introduced. Based on MaskNet, we design a Mask-DeepLab to make full use of HR. Last, the deep features of unlabeled areas are extracted by an autoencoder as auxiliary information, and those features are concatenated in the middle-level features extracted by Mask-DeepLab to alleviate the influences of overfitting caused by small-scale samples. The algorithm is verified on three HRCD datasets. The visualization and quantitative analysis of the experiment results figure that this algorithm can implement significant performance improvement.

Keywords

• Autoencoder
• Change detection (CD)
• DeepLabV3+
• High-resolution remote sensing images (HR)
• Mask