Abstract
With the advent of very-high-resolution remote sensing images, semantic change detection (SCD) based on deep learning has become a research hotspot in recent years. SCD aims to observe the change in the Earth’s land surface and plays a vital role in monitoring the ecological environment, land use and land cover. Existing research mainly focus on single-task semantic change detection; the problem they face is that existing methods are incapable of identifying which change type has occurred in each multi-temporal image. In addition, few methods use the binary change region to help train a deep SCD-based network. Hence, we propose a dual-task semantic change detection network (GCF-SCD-Net) by using the generative change field (GCF) module to locate and segment the change region; what is more, the proposed network is end-to-end trainable. In the meantime, because of the influence of the imbalance label, we propose a separable loss function to alleviate the over-fitting problem. Extensive experiments are conducted in this work to validate the performance of our method. Finally, our work achieves a 69.9% mIoU and 17.9 Sek on the SECOND dataset. Compared with traditional networks, GCF-SCD-Net achieves the best results and promising performances.
Highlights
Based on the abovementioned problems, our work proposes a novel dual-task semantic change detection Siamese network using the generative change field module to help the prediction of change regions and segmentation
generative change field (GCF)-semantic change detection (SCD)-Net inthe mean Intersection over Union (mIoU), the did not use the multiscale strategy to optimize the parameters of the models, we proposed networknetwork achievesachieves the bestthe results
In order to address the problem that existing methods are incapable of obtaining a significant result for dual-task semantic change detection, we proposed a generative change field (GCF)-based dual-task semantic change detection network for remote sensing images
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Based on the abovementioned problems, our work proposes a novel dual-task semantic change detection Siamese network using the generative change field module to help the prediction of change regions and segmentation. In previous work [19], Mou et al have proposed to use the binary change map to help model training, the proposed network exploits the convolutional neural network and recurrent neural network to achieve feature extraction and change detection, and obtains the binary change maps by using fully connected layers activated by the sigmoid function Their method only used the auxiliary loss method to generate a sematic change map. For the SCD task, change features play an important role in helping the model predict semantic change labels They can guide the generative semantic change map module to focus on different regions between bitemporal images. Method to guide two branch networks achieve dual-task semantic detecand no-change region, we propose a robust separable loss function that enables to improve tion.
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