Existing deep learning-based hyperspectral image (HSI) classification works still suffer from the limitation of the fixed-sized receptive field, leading to difficulties in distinctive spectral-spatial features for ground objects with various sizes and arbitrary shapes. Meanwhile, plenty of previous works ignore asymmetric spectral-spatial dimensions in HSI. To address the above issues, we propose a multi-stage search architecture in order to overcome asymmetric spectral-spatial dimensions and capture significant features. First, the asymmetric pooling on the spectral-spatial dimension maximally retains the essential features of HSI. Then, the 3D convolution with a selectable range of receptive fields overcomes the constraints of fixed-sized convolution kernels. Finally, we extend these two searchable operations to different layers of each stage to build the final architecture. Extensive experiments are conducted on two challenging HSI benchmarks including Indian Pines and Houston University, and results demonstrate the effectiveness of the proposed method with superior performance compared with the related works.

翻译：摘要：现有基于深度学习的高光谱图像分类方法仍受限于固定尺寸的感受野，导致难以提取具有不同尺寸和任意形状地物的差异化光谱-空间特征。同时，许多先前工作忽视了高光谱数据中非对称的光谱-空间维度。为解决上述问题，我们提出一种多阶段搜索架构，以克服非对称光谱-空间维度并捕获关键特征。首先，通过光谱-空间维度的非对称池化最大程度保留高光谱图像的本质特征；其次，采用具有可选择感受野范围的3D卷积突破固定尺寸卷积核的限制；最后，将这两种可搜索操作扩展至每个阶段的不同层以构建最终架构。在两个具有挑战性的高光谱基准数据集（Indian Pines和Houston University）上进行了大量实验，结果表明所提方法相比相关方法具有更优性能，验证了其有效性。