Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional Neural Network (CNN) is one of the most frequently used deep learning based methods for visual data processing. The use of CNN for HSI classification is also visible in recent works. These approaches are mostly based on 2D CNN. Whereas, the HSI classification performance is highly dependent on both spatial and spectral information. Very few methods have utilized the 3D CNN because of increased computational complexity. This letter proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI classification. Basically, the HybridSN is a spectral-spatial 3D-CNN followed by spatial 2D-CNN. The 3D-CNN facilitates the joint spatial-spectral feature representation from a stack of spectral bands. The 2D-CNN on top of the 3D-CNN further learns more abstract level spatial representation. Moreover, the use of hybrid CNNs reduces the complexity of the model compared to 3D-CNN alone. To test the performance of this hybrid approach, very rigorous HSI classification experiments are performed over Indian Pines, Pavia University and Salinas Scene remote sensing datasets. The results are compared with the state-of-the-art hand-crafted as well as end-to-end deep learning based methods. A very satisfactory performance is obtained using the proposed HybridSN for HSI classification. The source code can be found at \url{https://github.com/gokriznastic/HybridSN}.
翻译:超光谱图像(HISI)分类被广泛用于分析遥感图像。超光谱图像包括不同的图像波段。进化神经网络(CNN)是用于视觉数据处理的最常用的深层次学习方法之一。使用CNN进行HSI分类在最近的作品中也可以看到。这些方法大多以2DCNN为基础。虽然HSI分类性能高度依赖空间和光谱信息。由于计算复杂性的增加,很少有方法使用3DCNN。由于计算复杂性的增加,因此使用3DCNN。本信提议为HSI分类建立一个混合光谱谱谱神经网络(HybridSN) 。基本上,MABNSN是光谱3D-CNN(3D-CNN) 最常用的深层学习方法之一。 3D-CNN(3D-CNN)有助于一组光谱频谱带的联合空间光谱特征展示。 3D-CNN(3D-CNN)进一步学会的空间代表性代表。使用混合网络网络网络网络网络网络网络网络网络网络网络网络(HCNNN) 与3NN(仅用于HNNN)分类分类。GMISDSISNNNNNN(HSN-SNN) 基本上,然后测试2-SNS-SNA-SNA-SNA-SNS(SN)的深度实验方法的运行。在SN-SNS-SNS-SNS-SNS-SL-SL-SL-SL-Syal-Syal-Syal-Syaltradealdaltradal-Sldrodaldaldal-leg-S)的模拟实验中, 和SI,在使用这种深层实验中进行非常的模拟的模拟的实验中进行。