Adaptive Context Transformer for Semisupervised Remote Sensing Image Segmentation

Li, Yunbo; Yi, Zhiyu; Wang, Yuebin; Zhang, Liqiang

doi:10.1109/tgrs.2023.3318788

Cited by 1 publication

(1 citation statement)

References 48 publications

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“…Compared with convolutional networks, the attention features extracted by transformers contain more contextual information and will not lose the detailed features of the samples during the downsampling process. Li proposed an adaptive contextual transformer model with an adjustable sliding window and designed a point-line-area pseudo-label filtering mechanism based on clustering and boundary extraction to filter unreliable pseudo-labels [20]. Ma proposed a new general image fusion framework based on cross-domain distance learning and SWIN Transformer, which achieves full integration of complementary information and global interaction through attention-guided cross-domain modules [21].…”

Section: Cross-domain Semantic Segmentation Algorithm For Remote Sens...mentioning

confidence: 99%

A Bio-Inspired Visual Perception Transformer for Cross-Domain Semantic Segmentation of High-Resolution Remote Sensing Images

Wang,

Jing

et al. 2024

Remote Sensing

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Pixel-level classification of very-high-resolution images is a crucial yet challenging task in remote sensing. While transformers have demonstrated effectiveness in capturing dependencies, their tendency to partition images into patches may restrict their applicability to highly detailed remote sensing images. To extract latent contextual semantic information from high-resolution remote sensing images, we proposed a gaze–saccade transformer (GSV-Trans) with visual perceptual attention. GSV-Trans incorporates a visual perceptual attention (VPA) mechanism that dynamically allocates computational resources based on the semantic complexity of the image. The VPA mechanism includes both gaze attention and eye movement attention, enabling the model to focus on the most critical parts of the image and acquire competitive semantic information. Additionally, to capture contextual semantic information across different levels in the image, we designed an inter-layer short-term visual memory module with bidirectional affinity propagation to guide attention allocation. Furthermore, we introduced a dual-branch pseudo-label module (DBPL) that imposes pixel-level and category-level semantic constraints on both gaze and saccade branches. DBPL encourages the model to extract domain-invariant features and align semantic information across different domains in the feature space. Extensive experiments on multiple pixel-level classification benchmarks confirm the effectiveness and superiority of our method over the state of the art.

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Section: Cross-domain Semantic Segmentation Algorithm For Remote Sens...mentioning

confidence: 99%

A Bio-Inspired Visual Perception Transformer for Cross-Domain Semantic Segmentation of High-Resolution Remote Sensing Images

Wang,

Jing

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

Adaptive Context Transformer for Semisupervised Remote Sensing Image Segmentation

Cited by 1 publication

References 48 publications

A Bio-Inspired Visual Perception Transformer for Cross-Domain Semantic Segmentation of High-Resolution Remote Sensing Images

A Bio-Inspired Visual Perception Transformer for Cross-Domain Semantic Segmentation of High-Resolution Remote Sensing Images

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