Multilevel Deformable Attention-Aggregated Networks for Change Detection in Bitemporal Remote Sensing Imagery

Zhang, Xiaokang; Yu, Weikang; Pun, Man-On

doi:10.1109/tgrs.2022.3157721

Cited by 19 publications

(9 citation statements)

References 51 publications

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“…To address the insensitivity of Transformer to position, Feng et al [48] introduced depth-wise convolutional relative position coding and proposed a CD network combining Transformer and CNN using the strategy of local and global feature fusion, achieving better CD results. To model contextual dependency among feature maps at different stages, Zhang et al [62] proposed a multi-level change-aware deformable attention.…”

Section: Attention Mechanism For Remote Sensing Image CDmentioning

confidence: 99%

Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images

Lei

Geng

Ning

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Deep convolutional neural networks have achieved much success in remote sensing image change detection (CD) but still suffer from two main problems. First, existing multi-scale feature fusion methods often employ redundant feature extraction and fusion strategies, which often leads to high computational costs and memory usage. Second, the regular attention mechanism in CD is difficult to model spatial-spectral features and generate 3D attention weights at the same time, ignoring the cooperation between spatial features and spectral features. To address the above issues, an efficient ultra-lightweight spatialspectral feature cooperation network (USSFC-Net) is proposed for CD in this paper. The proposed USSFC-Net has two main advantages. First, a multi-scale decoupled convolution (MSDConv) is designed, which is clearly different from the popular atrous spatial pyramid pooling (ASPP) module and its variants since it can flexibly capture the multi-scale features of changed objects by using cyclic multi-scale convolution. Meanwhile, the design of MSDConv can greatly reduce the number of parameters and computational redundancy. Second, an efficient spatial-spectral feature cooperation strategy (SSFC) is introduced to obtain richer features. The SSFC differs from existing 2D attention mechanisms since it learns 3D spatial-spectral attention weights without adding any parameters. The experiments on three datasets for remote sensing image CD demonstrate that the proposed USSFC-Net achieves better CD accuracy than most convolutional neural

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Section: Attention Mechanism For Remote Sensing Image CDmentioning

confidence: 99%

Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images

Lei

Geng

Ning

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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show abstract

“…Despite a large amount of RS data, high-quality labels via manual annotation could be costly. Many efforts in RS CD have been made to tackle the label insufficiency, including applying data augmentation [37][38][39][40][41][42][43], generating pseudo labels for unlabeled data via semisupervised learning [36,44,45], using active learning to select a small number of informative samples [46][47][48][49], and finetuning a pre-trained model [8,10,16,17,19,50,51].…”

Section: A Handling Label Insufficiency In CDmentioning

confidence: 99%

“…Data augmentation is an effective solution to enhance the size of the training dataset. The most common way is to use transformation-based augmentations [37,[39][40][41][42][43], including geometric transformations (e.g., random crop, horizontal flip), color transformations, and Gaussian blur, etc. A recent advance increases the number of the positive samples (change of interest) by blending the gan-generated instance on the appropriate spatial-temporal position of the bitemporal image [38].…”

Section: A Handling Label Insufficiency In CDmentioning

confidence: 99%

Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection

Chen,

Li,

Chen

et al. 2022

Preprint

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Training deep learning-based change detection (CD) model heavily depends on labeled data. Contemporary transfer learning-based methods to alleviate the CD label insufficiency mainly upon ImageNet pre-training. A recent trend is using remote sensing (RS) data to obtain in-domain representations via supervised or self-supervised learning (SSL). Here, different from traditional supervised pre-training that learns the mapping from image to label, we leverage semantic supervision in a contrastive manner. There are typically multiple objects of interest (e.g., buildings) distributed in varying locations in RS images. We propose dense semantic-aware pre-training for RS image CD via sampling multiple class-balanced points. Instead of manipulating image-level representations that lack spatial information, we constrain pixel-level cross-view consistency and cross-semantic discrimination to learn spatially-sensitive features, thus benefiting downstream dense CD. Apart from learning illumination invariant features, we fulfill consistent foreground features insensitive to irrelevant background changes via a synthetic view using background swapping. We additionally achieve discriminative representations to distinguish foreground land-covers and other backgrounds. We collect large-scale image-mask pairs freely available in the RS community for pre-training. Extensive experiments on three CD datasets verify the effectiveness of our method. Ours significantly outperforms ImageNet, in-domain supervision, and several SSL methods. Empirical results indicate ours well alleviates data insufficiency in CD. Notably, we achieve competitive results using only 20% training data than random baseline using 100% data. Both quantitative and qualitative results demonstrate the generalization ability of our pre-trained model to downstream images even remaining domain gaps with the pre-training data. Our data and code will make public.

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“…Enhancements in satellite technologies have greatly expanded the applications of Remote Sensing (RS) imagery in areas such as disaster relief, geology, environment, and engineering construction [1][2][3][4]. Despite these advancements, challenges persist due to limitations in imaging instruments and long-range shooting, resulting in RS satellite images with resolutions that cannot fully meet the requirements for downstream applications, especially on semantic segmentation tasks [5,6].…”

Section: Introductionmentioning

confidence: 99%

Denoising Diffusion Probabilistic Model with Adversarial Learning for Remote Sensing Super-Resolution

Sui,

Wu,

Pun

2024

Remote Sensing

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Single Image Super-Resolution (SISR) for image enhancement enables the generation of high spatial resolution in Remote Sensing (RS) images without incurring additional costs. This approach offers a practical solution to obtain high-resolution RS images, addressing challenges posed by the expense of acquisition equipment and unpredictable weather conditions. To address the over-smoothing of the previous SISR models, the diffusion model has been incorporated into RS SISR to generate Super-Resolution (SR) images with enhanced textural details. In this paper, we propose a Diffusion model with Adversarial Learning Strategy (DiffALS) to refine the generative capability of the diffusion model. DiffALS integrates an additional Noise Discriminator (ND) into the training process, employing an adversarial learning strategy on the data distribution learning. This ND guides noise prediction by considering the general correspondence between the noisy image in each step, thereby enhancing the diversity of generated data and the detailed texture prediction of the diffusion model. Furthermore, considering that the diffusion model may exhibit suboptimal performance on traditional pixel-level metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM), we showcase the effectiveness of DiffALS through downstream semantic segmentation applications. Extensive experiments demonstrate that the proposed model achieves remarkable accuracy and notable visual enhancements. Compared to other state-of-the-art methods, our model establishes an improvement of 189 for Fréchet Inception Distance (FID) and 0.002 for Learned Perceptual Image Patch Similarity (LPIPS) in a SR dataset, namely Alsat, and achieves improvements of 0.4%, 0.3%, and 0.2% for F1 score, MIoU, and Accuracy, respectively, in a segmentation dataset, namely Vaihingen.

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Multilevel Deformable Attention-Aggregated Networks for Change Detection in Bitemporal Remote Sensing Imagery

Cited by 19 publications

References 51 publications

Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images

Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images

Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection

Denoising Diffusion Probabilistic Model with Adversarial Learning for Remote Sensing Super-Resolution

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