用小波域卷积提升高光谱图像分类，兼顾精度与小样本表现。
CWSSNet: Hyperspectral Image Classification Enhanced by Wavelet Domain Convolution
- 在小波域进行多波段分解与卷积，融合光谱空间特征
- 在余干县测试中达mIoU 74.50%，mAcc 82.73%，mF1 84.94%
- 小样本训练下仍稳定，适合数据稀缺场景
高光谱遥感技术在林业生态与精准农业等领域具有重要应用价值，但对细粒度地物分类提出更高要求。尽管高光谱图像富含光谱信息，可提升识别精度，却因波段众多、维度高及光谱混合特性导致显著特征冗余。本研究以资源一号福星（ZY1F）卫星的高光谱数据为源，选取江西省上饶市余干县作为研究区，开展地物分类研究。提出一种名为CWSSNet的分类框架，融合三维光谱-空间特征与小波域卷积。该框架通过多尺度卷积注意力模块整合多模态信息，并在小波域引入多波段分解与卷积操作，突破传统方法的分类性能瓶颈。实验表明，CWSSNet在余干县测试中，平均交并比（mIoU）达74.50%，平均准确率（mAcc）为82.73%，平均F1分数（mF1）为84.94%；在水体、植被、裸地分类中均取得最高交并比，表现良好鲁棒性。当训练集比例为70%时，训练时间增长有限，分类效果已接近最优，表明模型在小样本条件下仍具可靠性能。
原文摘要 · Abstract (English)
Hyperspectral remote sensing technology has significant application value in fields such as forestry ecology and precision agriculture, while also putting forward higher requirements for fine ground object classification. However, although hyperspectral images are rich in spectral information and can improve recognition accuracy, they tend to cause prominent feature redundancy due to their numerous bands, high dimensionality, and spectral mixing characteristics. To address this, this study used hyperspectral images from the ZY1F satellite as a data source and selected Yugan County, Shangrao City, Jiangxi Province as the research area to perform ground object classification research. A classification framework named CWSSNet was proposed, which integrates 3D spectral-spatial features and wavelet convolution. This framework integrates multimodal information us-ing a multiscale convolutional attention module and breaks through the classification performance bottleneck of traditional methods by introducing multi-band decomposition and convolution operations in the wavelet domain. The experiments showed that CWSSNet achieved 74.50\%, 82.73\%, and 84.94\% in mean Intersection over Union (mIoU), mean Accuracy (mAcc), and mean F1-score (mF1) respectively in Yugan County. It also obtained the highest Intersection over Union (IoU) in the classifica-tion of water bodies, vegetation, and bare land, demonstrating good robustness. Additionally, when the training set proportion was 70\%, the increase in training time was limited, and the classification effect was close to the optimal level, indicating that the model maintains reliable performance under small-sample training conditions.
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