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Suting Chen

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2 papers

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EAAI Journal 2026 Journal Article

GMFIMamba: Remote sensing change detection based on group Mamba feature interaction

Wenliang Xu
Suting Chen
Feilong Bi
Chao Wang
Xiao Shu

With the advancement of satellite technology, high-resolution remote sensing images have been widely used in the field of change detection. Building Change Detection (BCD) and Building Damage Assessment (BDA) are both sub-tasks of change detection. BCD aims to detect structural changes in buildings over time, whereas BDA focuses on assessing the level of building damage after a disaster. BCD is of great value for urban planning, while BDA plays a crucial role in post-disaster rescue efforts. To address these tasks, we propose a change detection method based on Mamba, named GMFIMamba. Specifically, we design a Convolution–Visual State Space (Conv-VSS) block, which combines the local feature extraction capability of Convolutional Neural Networks (CNNs) with the global feature modeling ability of Mamba. By integrating local and global features, our approach improves the accuracy of change region detection. To tackle the issue of insufficient feature extraction for small-scale buildings in existing models, we introduce the Multi-branch Dilated Convolution Feature Enhancement Module (MCFEM). In addition, we design the Grouped Mamba-Based Bitemporal Features Interaction Module (GMBFIM) to facilitate effective interaction between bitemporal images, leading to more accurate change feature extraction. Experiments on three public datasets demonstrate that the proposed method achieves superior performance in both BCD and BDA tasks, proving its effectiveness.

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EAAI Journal 2024 Journal Article

Dual-branch deep cross-modal interaction network for semantic segmentation with thermal images

Kang Dai
Suting Chen

Semantic segmentation using RGB (Red-Green-Blue) images and thermal datas is an indispensable component of autonomous driving. The key to RGB-Thermal (RGB and Thermal) semantic segmentation is achieving the interaction and fusion of features between RGB and thermal images. Therefore, we propose a dual-branch deep cross-modal interaction network (DCIT) based on Encoder–Decoder structure. This framework consists of two parallel networks for feature extraction from RGB and Thermal data. Specifically, in each feature extraction stage of the Encoder, we design a Cross Feature Regulation Modules (CFRM) to align and correct modality specific features by reducing the inter-modality feature differences and eliminating intra-modality noise. Then, the modality features are aggregated through Cross Modal Feature Fusion Module (CMFFM) based on cross linear attention to capture global information from modality features. Finally, Adaptive Multi-Scale Cross-positional Fusion Module (AMCFM) utilizes the fused features to integrate consistent semantic information in the Decoder stage. Our framework can improve the interaction of cross modal features. Extensive experiments on urban scene datasets demonstrate that our proposed framework outperforms other RGB-Thermal semantic segmentation methods in terms of objective metrics and subjective visual assessments.

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