融合多维度CNN的高光谱遥感图像分类算法

Abstract

Objective: Now hyperspectral images have high spatial and spectral resolution, and play an important role in the fields of land monitoring, environmental protection, earthquake prevention, and disaster reduction. However, the high dimensionality and large data volume of hyperspectral data bring many problems (e.g., strong correlation among bands, mixed redundant pixels, and data information redundancy) to hyperspectral classification. With the continuous development of deep learning technology, the convolutional neural network (CNN), as one of its representative algorithms, provides a new solution for hyperspectral image classification. There are three common hyperspectral classification methods based on the CNN network. Among them, 1D CNN extracts spectral information, and 2D CNN extracts spatial information. In contrast, 3D CNN is usually composed of three-dimensional convolution kernels, which can extract two-dimensional spatial features and one-dimensional spectral features at the same time. Although 3D CNN has a better effect in spatial-spectral information fusion, this model is more complex, which increases the cost of network calculation and the number of parameters. With the rapid expansion of data volume, the classification accuracy and speed of a complex model are not satisfactory. Here we propose a lightweight fusion CNN algorithm, 3D-2D-1D CNN, for hyperspectral image classification. This algorithm organically integrates CNNs of different dimensions, reduces the calculation amount of 3D CNN operations, and makes full use of the hyperspectral spatial-spectral joint information. We hope that our basic strategy and findings can be helpful to improve the applicability and computational efficiency of the model. Methods: A hybrid algorithm 3D-2D-1D CNN model (Fig. 1) is described as follows. Firstly, the hyperspectral data is processed by 3D CNN. In the hyperspectral cube, a three-dimensional convolution kernel is used for convolution calculation. Each feature

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