Journal Article10.1109/TPAMI.2023.3237896
Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection
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TL;DR: A fully convolutional change detection framework with generative adversarial network is proposed, to conclude unsupervised, weakly supervised, regional supervised, and fully supervised change detection tasks into one framework.
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Abstract: Deep learning for change detection is one of the current hot topics in the field of remote sensing. However, most end-to-end networks are proposed for supervised change detection, and unsupervised change detection models depend on traditional pre-detection methods. Therefore, we proposed a fully convolutional change detection framework with generative adversarial network, to unify unsupervised, weakly supervised, regional supervised, and fully supervised change detection tasks into one end-to-end framework. A basic Unet segmentor is used to obtain change detection map, an image-to-image generator is implemented to model the spectral and spatial variation between multi-temporal images, and a discriminator for changed and unchanged is proposed for modeling the semantic changes in weakly and regional supervised change detection task. The iterative optimization of segmentor and generator can build an end-to-end network for unsupervised change detection, the adversarial process between segmentor and discriminator can provide the solutions for weakly and regional supervised change detection, the segmentor itself can be trained for fully supervised task. The experiments indicate the effectiveness of the propsed framework in unsupervised, weakly supervised and regional supervised change detection. This article provides new theorical definitions for unsupervised, weakly supervised and regional supervised change detection tasks with the proposed framework, and shows great potentials in exploring end-to-end network for remote sensing change detection (https://github.com/Cwuwhu/FCD-GAN-pytorch).
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Citations
Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery
Chengxi Han,Chengwei Wu,Haonan Guo,Meiqi Hu,Jiepan Li,Hongruixuan Chen +5 more
TL;DR: The change guiding network (CGNet) is designed to tackle the insufficient expression problem of change features in the conventional U-Net structure adopted in previous methods, which causes inaccurate edge detection and internal holes, and a self-attention module named change guide module is proposed, which can effectively capture the long-distance dependency among pixels.
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Hierarchical Attention Feature Fusion-Based Network for Land Cover Change Detection With Homogeneous and Heterogeneous Remote Sensing Images
Zhiyong Lv,Jie Liu,Weiwei Sun,Tao Lei,Jon Atli Benediktsson,Xiuping Jia +5 more
TL;DR: In the proposed HAFF-based network, novel multiscale convolution fusion filters (MCFFs) explore the global semantic feature of the interested targets from multiperspective ways, and quantitative observations indicated that competitive improvements are achieved by the proposed MCFFs in terms of all the evaluation indicators.
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HyperNet: Self-Supervised Hyperspectral Spatial–Spectral Feature Understanding Network for Hyperspectral Change Detection
TL;DR: Huang et al. as discussed by the authors proposed a pixel-level self-supervised hyperspectral spatial-spectral feature understanding network (HyperNet) to accomplish pixelwise feature representation.
34
DDPM-CD: Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models
TL;DR: Wang et al. as discussed by the authors proposed a change detection (CD)/ segmentation method based on denoising diffusion probabilistic models to detect precise changes on Earth's surface.
25
UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation
TL;DR: A novel unsupervised CD method using a lightweight transformer, called UCDFormer, which achieves excellent performance for earthquake-induced landslide detection when considering large-scale applications and improves performance on the Kappa coefficient by more than 12%.
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