Comparison with base-line deep learning methods.

Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size.

高光谱图像（Hyperspectral Images, HSI）分类是一项极具挑战性的任务：这类图像拥有海量空谱波段，波段间相似性极高，类别差异显著，且区域关系复杂，涵盖重叠区域与嵌套区域，使得高光谱遥感图像的分类成为一项复杂问题。卷积神经网络（Convolutional Neural Networks, CNN）凭借聚焦高光谱数据分类，在解决该挑战的过程中获得了广泛应用。然而，二维卷积神经网络（2D-CNN）方法的性能高度依赖空间信息，而三维卷积神经网络（3D-CNN）则通过同时兼顾光谱与空间信息，提供了另一种解决方案。尽管如此，由于参数量规模庞大且需处理光谱维度信息，三维卷积神经网络方法的计算复杂度显著提升，同时还面临难以处理特征图的局部固有细节模式信息、难以开展低秩频率特征调优等难题。为克服上述挑战并提升高光谱图像分类性能，本文提出了一种创新方法——注意力三维中心差分卷积密集网络（Attention 3D Central Difference Convolutional Dense Network，3D-CDC Attention DenseNet）。该方法通过在密集连接策略中采用逐像素拼接与空间注意力机制，实现对空谱特征图局部固有细节模式的有效处理，以此融合低秩频率特征并指导特征调优。在帕维亚大学（Pavia University）、休斯顿2018（Houston 2018）与印第安纳松园（Indian Pines）等基准数据集上的实验结果表明，相较于包括当前最优技术在内的其他高光谱图像分类方法，本文所提方法具有显著性能优势：当采用25×25窗口尺寸时，该方法在休斯顿2018数据集上取得97.93%的总体分类精度，在帕维亚大学数据集上取得99.89%的精度，在印第安纳松园数据集上则取得99.38%的精度。