The Classification of Hyperspectral Images Based on Band-Grouping and Convolutional Neural Network

Abstract

For the classification of hyperspectral images, a classification algorithm based on band grouping and three-dimensional convolutional neural network(3D-CNN-BG) is proposed. The algorithm uses the correlation matrix of hyperspectral images to determine the similarity of bands, and the high similarity bands are grouped together. Then, every bands group is extracted spatial-spectral feature using 3D convolutional neural network. Finally, the high-level feature of every 3D-CNN is stacked together trained by the classifier. With band regroup, the feature in the similar group can be extracted fully by the 3D-CNN, which is good for classification. The experiments showed that compared with other hyperspectral images classification algorithms based on CNN, the proposed algorithm converges more faster, has less training parameters, and acquires higher classification accuracy. After training 100 times, the overall classification accuracy on the indian pines data set can reach 97.42%, increased by approximately 2% ~ 5%, which shows the strong practicability of proposed algorithm in hyperspectral application.

Composite Clustering Sampling Strategy for Multiscale Spectral-Spatial Classification of Hyperspectral Images

“…When CNNs were first used for HSI classification, models only used the spectral feature information of pixels [11,12], which largely wastes an advantage of HSIs; that is, spatial information and spectral information are closely combined. In response to this problem, a spatial-spectral classification method was proposed [13], and the spatial position information and spectral information of pixels were full used during model training, which effectively improved classification performance of CNN models. Therefore, the spatial-spectral classification method has been widely used [14,15].…”

Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network

“…To avoid this problem, many scholars have selected to adopt the largest pooling method. For example, Serre et al applied two-dimensional (2D) maximum pooling for optimization [ 16 ], and Fu et al proposed a 3D maximum pooling method [ 17 ]. However, these researchers did not observe the effect of the relationship between the step and pooling nuclear sizes on classification accuracy.…”