MCCRNet: A Multi-Level Change Contextual Refinement Network for Remote Sensing Image Change Detection

Abstract

Change detection based on bi-temporal remote sensing images has made significant progress in recent years, aiming to identify the changed and unchanged pixels between a registered pair of images. However, most learning-based change detection methods only utilize fused high-level features from the feature encoder and thus miss the detailed representations that low-level feature pairs contain. Here we propose a multi-level change contextual refinement network (MCCRNet) to strengthen the multi-level change representations of feature pairs. To effectively capture the dependencies of feature pairs while avoiding fusing them, our atrous spatial pyramid cross attention (ASPCA) module introduces a crossed spatial attention module and a crossed channel attention module to emphasize the position importance and channel importance of each feature while simultaneously keeping the scale of input and output the same. This module can be plugged into any feature extraction layer of a Siamese change detection network. Furthermore, we propose a change contextual representations (CCR) module from the perspective of the relationship between the change pixels and the contextual representation, named change region contextual representations. The CCR module aims to correct changed pixels mistakenly predicted as unchanged by a class attention mechanism. Finally, we introduce an effective sample number adaptively weighted loss to solve the class-imbalanced problem of change detection datasets. On the whole, compared with other attention modules that only use fused features from the highest feature pairs, our method can capture the multi-level spatial, channel, and class context of change discrimination information. The experiments are performed with four public change detection datasets of various image resolutions. Compared to state-of-the-art methods, our MCCRNet achieved superior performance on all datasets (i.e., LEVIR, Season-Varying Change Detection Dataset, Google Data GZ, and DSIFN) with improvements of 0.47%, 0.11%, 2.62%, and 3.99%, respectively.

Masnet: Improve Performance of Siamese Networks With Mutual-Attention for Remote Sensing Change Detection Tasks

“…Our best model is selected from the 3 repeated tests as mentioned above, and inference with extra TTA, in which we set the inference multi-scale to be [0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0]. methods F1 score IoU score FCN+PAM (Chen, 2020a) 87.3 --CANet (Lu, 2021) 87.4 --Multibranch (Zhao, 2021) 88.04 --FODA 88.73 --MCCRNet (Ke, 2021) 90.71 --ChangeStar (Zhen, 2021) 91.25 83.92 CEECNet V2 (Diakogiannis, 2021) 91.83 84.89 FCCDN (Chen, 2021c) 92.29 85.69 MASNet (ours) 92.62 86.26…”

MDFENet: A Multiscale Difference Feature Enhancement Network for Remote Sensing Change Detection

“…Zhang et al [40] proposed a deep-supervised feature fusion network that fuses deep and difference features to ensure the integrity of the boundary, and introduced a deep-supervised method to train the model to alleviate the vanishing gradient problem. Ke et al [41] proposed a MCCRNet for change detection. In recent years, multi-task methods [42]- [47] have become popular, that is, to perform semantic segmentation and CD simultaneously on bitemporal remote sensing images, and the segmentation and CD can optimize and improve each other.…”

SiameseNet Based Fine-Grained Semantic Change Detection for High Resolution Remote Sensing Images

“…The existing change detection methods can be divided into the image difference method [8,9], change vector analysis (CVA) [10,11], principal component analysis (PCA) [12,13], and the deep learning method [6,7,[14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

“…Experiments show that the attention module of STANet can reduce the detection error caused by improper registration of multitemporal remote sensing images. Ke et al [17] proposed a multi-level change context refinement network (MCCRNet), which introduces a change context module (CCR) to capture denser change information between dual-temporal remote sensing images. Peng et al [18] proposed a difference-enhancement dense-attention convolutional neural network (DDCNN), which combines dense attention and image difference to improve the effectiveness of the network and its accuracy in extracting the change features.…”