Boundary-Aware Multi-Scale Feature Enhancement Based Few-Shot Hyperspectral Image Semantic Segmentation.
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| Title: | Boundary-Aware Multi-Scale Feature Enhancement Based Few-Shot Hyperspectral Image Semantic Segmentation. |
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| Authors: | Zhang, Xiaorong1 (AUTHOR) zhangxiaorong@opt.ac.cn, Li, Siyuan1,2 (AUTHOR), Zheng, Xi1,2 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1911. 22p. |
| Subjects: | Image segmentation, Signal convolution, Machine learning, Generalization, Remote sensing, Mathematical convolutions |
| Abstract: | Highlights: What are the main findings? A few-shot hyperspectral image semantic segmentation framework based on edge-aware multi-scale enhancement, integrating orthogonal-direction convolutions, slender feature enhancement, cross-stage feature fusion, and deep supervision mechanisms was proposed. The framework effectively addresses challenges of hyperspectral image segmentation under few-shot conditions and the poor accuracy of slender geographic objects, significantly improving feature utilization and model generalization ability. What are the implications of the main findings? This study provides a new solution for hyperspectral image analysis under few-shot conditions. To address the issues of model overfitting under scarce samples and poor segmentation performance on slender objects in the task of semantic segmentation of remote sensing hyperspectral images, this paper proposes a hyperspectral image semantic segmentation framework that integrates edge awareness and multi-scale feature enhancement under extremely few-shot conditions. This architecture effectively integrates orthogonal-direction convolutions, elongated feature enhancement, multi-scale feature fusion, and deep supervision mechanisms, solving challenges such as difficulty in extracting features of slender objects, model overfitting under few-sample conditions, and insufficient generalization ability. The experimental results on multiple public datasets show that the proposed algorithm achieves excellent segmentation performance with just one small-sized sample per labeled category, surpassing existing popular algorithms and thereby confirming the algorithm's effectiveness and superiority. On the PaviaU dataset, the overall accuracy (OA) and mean intersection over union (mIoU) improved by approximately 9.7% and 15.5% compared to the second-best model; especially for the segmentation of the key elongated feature 'road', the intersection over union reached 94.75%, highlighting the effectiveness of the proposed mechanism. This paper provides a novel and efficient solution for fine interpretation of hyperspectral images under few-sample conditions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A few-shot hyperspectral image semantic segmentation framework based on edge-aware multi-scale enhancement, integrating orthogonal-direction convolutions, slender feature enhancement, cross-stage feature fusion, and deep supervision mechanisms was proposed. The framework effectively addresses challenges of hyperspectral image segmentation under few-shot conditions and the poor accuracy of slender geographic objects, significantly improving feature utilization and model generalization ability. What are the implications of the main findings? This study provides a new solution for hyperspectral image analysis under few-shot conditions. To address the issues of model overfitting under scarce samples and poor segmentation performance on slender objects in the task of semantic segmentation of remote sensing hyperspectral images, this paper proposes a hyperspectral image semantic segmentation framework that integrates edge awareness and multi-scale feature enhancement under extremely few-shot conditions. This architecture effectively integrates orthogonal-direction convolutions, elongated feature enhancement, multi-scale feature fusion, and deep supervision mechanisms, solving challenges such as difficulty in extracting features of slender objects, model overfitting under few-sample conditions, and insufficient generalization ability. The experimental results on multiple public datasets show that the proposed algorithm achieves excellent segmentation performance with just one small-sized sample per labeled category, surpassing existing popular algorithms and thereby confirming the algorithm's effectiveness and superiority. On the PaviaU dataset, the overall accuracy (OA) and mean intersection over union (mIoU) improved by approximately 9.7% and 15.5% compared to the second-best model; especially for the segmentation of the key elongated feature 'road', the intersection over union reached 94.75%, highlighting the effectiveness of the proposed mechanism. This paper provides a novel and efficient solution for fine interpretation of hyperspectral images under few-sample conditions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121911 |
