Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network

Abstract

Timely and accurate change detection on satellite images by using computer vision techniques has been attracting lots of research efforts in recent years. Existing approaches based on deep learning frameworks have achieved good performance for the task of change detection on satellite images. However, under the scenario of disjoint changed areas in various shapes on land surface, existing methods still have shortcomings in detecting all changed areas correctly and representing the changed areas boundary. To deal with these problems, we design a coarse-to-fine detection framework via a boundary-aware attentive network with a hybrid loss to detect the change in high resolution satellite images. Specifically, we first perform an attention guided encoder-decoder subnet to obtain the coarse change map of the bi-temporal image pairs, and then apply residual learning to obtain the refined change map. We also propose a hybrid loss to provide the supervision from pixel, patch, and map levels. Comprehensive experiments are conducted on two benchmark datasets: LEBEDEV and SZTAKI to verify the effectiveness of the proposed method and the experimental results show that our model achieves state-of-the-art performance.

HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images

“…(3) The fully convolutional network pyramid pooling (FCN-PP) [31] and deep Siamese multiScale fully convolutional network (DSMS-FCN) [5] involved multiscale designs based on the previous baselines, by pyramid pooling and multiscale convolutional kernels unit, respectively; (4) The change detection based on UNet++ with multiple side-outputs fusion(UNet++MSOF) design a multiple side loss supervision on features densely upsampled from multiple scales in the UNet++; (5) The IFN [1] and boundary-aware attentive network (BA 2 Net) [36] involve attention mechanisms in the decoding process also deep supervision and refined detection to deal with features in different scales, based on late fusion and early fusion respectively; (6) The spatial-temporal attention-based network (STANet) [33] is based on late fusion and introduces pyramid pooling involved attention modules to adapt multiscale features. For quantitative comparisons, the evaluation metrics were calculated and summarized as shown in Tables 1 and 2, on LEBEDEV and LEVIR-CD, respectively.…”

“…Benefiting from the densely learning structure, this network achieves good robustness in detection precision. Zhang et al [36] propose a coarse-to-fine changedetection framework via high-level features guided network to use context information to better locate more change areas. The final change maps are refined by a residual learning subnetwork to use the low-level features.…”

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

“…This is one of the challenging tasks and with the increasing amount of multi-temporal RS images has become more popular. At-DL was used in 7 papers to detect changes in general [110,111], in buildings [51], or any other objects [81,112]. (vi) Other tasks, such as image dehazing [113], digital elevation model (DEM) void filling [114], and SAR image despeckling [115] were addressed with At-DL in 9 papers.…”

AGCDetNet:An Attention-Guided Network for Building Change Detection in High-Resolution Remote Sensing Images

“…Alternatively, the attention mechanism [52] has been widely studied and embedded into deep CNNs in many computer vision tasks [53]- [62]. To the best of our knowledge, works that utilize the attention mechanism for CD based on RS images are not sufficient [6], [35], [37], [38], [63], [64]. Specifically, the self-attention mechanism [53] is effective in modeling longrange dependencies and generating discriminative features.…”