PDF(Pubmed)
Abstract：
Hyperspectral image (HSI) classification is a highly challenging task, particularly in fields like crop yield prediction and agricultural infrastructure detection. These applications often involve complex image types, such as soil, vegetation, water bodies, and urban structures, encompassing a variety of surface features. In HSI, the strong correlation between adjacent bands leads to redundancy in spectral information, while using image patches as the basic unit of classification causes redundancy in spatial information. To more effectively extract key information from this massive redundancy for classification, we innovatively proposed the CESA-MCFormer model, building upon the transformer architecture with the introduction of the Center Enhanced Spatial Attention (CESA) module and Morphological Convolution (MC). The CESA module combines hard coding and soft coding to provide the model with prior spatial information before the mixing of spatial features, introducing comprehensive spatial information. MC employs a series of learnable pooling operations, not only extracting key details in both spatial and spectral dimensions but also effectively merging this information. By integrating the CESA module and MC, the CESA-MCFormer model employs a \"Selection-Extraction\" feature processing strategy, enabling it to achieve precise classification with minimal samples, without relying on dimension reduction techniques such as PCA. To thoroughly evaluate our method, we conducted extensive experiments on the IP, UP, and Chikusei datasets, comparing our method with the latest advanced approaches. The experimental results demonstrate that the CESA-MCFormer achieved outstanding performance on all three test datasets, with Kappa coefficients of 96.38%, 98.24%, and 99.53%, respectively.
摘要：
高光谱图像(HSI)分类是一项极具挑战性的任务,特别是在作物产量预测和农业基础设施检测等领域。这些应用程序通常涉及复杂的图像类型，比如土壤,植被,水体,和城市结构，包含各种表面特征。在HSI,相邻波段之间的强相关性导致光谱信息冗余，而使用图像块作为分类的基本单位会导致空间信息的冗余。为了更有效地从这种大量冗余中提取关键信息进行分类，我们创新性地提出了CESA-MCFormer模型，通过引入中心增强空间注意力（CESA）模块和形态卷积（MC）来构建变压器架构。CESA模块结合了硬编码和软编码，在混合空间特征之前为模型提供先验空间信息，引入全面的空间信息。MC采用了一系列可学习的池化操作，不仅提取空间和光谱维度的关键细节，而且有效地合并这些信息。通过集成CESA模块和MC，CESA-MCFormer模型采用了“选择-提取”特征处理策略，使其能够以最少的样本实现精确的分类，而不依赖于PCA等降维技术。为了彻底评估我们的方法，我们对IP进行了广泛的实验，UP,和Chikusei数据集，将我们的方法与最新的先进方法进行比较。实验结果表明，CESA-MCFormer在所有三个测试数据集上都取得了出色的性能，Kappa系数为96.38%，98.24%,99.53%,分别。
