DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images

Abstract

Change detection is a basic task of remote sensing image processing. The research objective is to identify the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise in deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudochange information. To overcome the lack of resistance in current methods to pseudochanges, in this article, we propose a new method, namely, dual attentive fully convolutional Siamese networks, for change detection in high-resolution images. Through the dual attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e., unchanged samples are much more abundant than changed samples, which is one of the main reasons for pseudochanges. We propose the weighted double-margin contrastive loss to address this problem by punishing attention to unchanged feature pairs and increasing attention to changed feature pairs. The experimental results of our method on the change detection dataset and the building change detection dataset demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.9% and 4.2%, respectively, in the F1 score. Our PyTorch implementation is available at https://github.com/lehaifeng/DASNet.

Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

“…Reference [49] utilized CNN to extract features first, and then low-rank decomposition and threshold operation were performed to obtain the result. References [50][51][52][53] utilized deep Siamese convolutional network to extract deep features for images separately. The distances between features were then calculated.…”

Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos

“…To make full use of the extracted deep representations, we employ the attention mechanism to capture the spatio-temporal dependencies of multi-temporal and multilevel deep representations [50]. The channel attention is used to exploit the inter-channel relationship of multi-temporal features and the spatial attention focusses on identifying the informative part along the spatial axis considering the importance of each pixel location [51].…”

Detection of Removed Objects in 3D Meshes Using Up-to-Date Images for Mixed-Reality Applications

“…In [22], such networks are employed to compare two co-registered RGB or multi-spectral aerial images. More recent work has focused on developing robustness to pseudo-changes [23]. Other works have built on the foundation of Siamese networks to generalize the process, such as in [24], where picture registration is not mandatory, and in [25], where object segmentation is performed, making the approach more robust to changes in weather or scene illumination.…”