Hyperspectral Image Classification Algorithm Based on Locally Retained Reduced Dimensional Convolution Neural Network

In order to improve the classification accuracy of hyperspectral remote sensing images, a novel hyperspectral image classification algorithm based on local preserving reduced dimensional convolutional neural network (DCNN) was proposed by using local preserving discriminant analysis and deep convolutional neural network (DCNN) algorithm. Firstly, the dimensionality reduction of hyperspectral data was analyzed by local reserved discriminant, and then the spatial tunnel information was filtered by two-dimensional Gabor filter. Secondly, the original hyperspectral data were extracted by convolution neural network to generate spectral tunnel information. Thirdly, the spatial tunnel information and spectral tunnel information were integrated to form the air-spectrum characteristic information, which was input into deep convolutional neural network to extract more effective features. Finally, the feature of the final extraction was classified by using the dual optimization classifier. The proposed method was compared with CNN, PCA-SVM, CD-CNN and CNN-PPF in the performance of Indian Pines and University of Pavia hyperspectral remote sensing databases. In the database of Indian Pines and University of Pavia, the overall recognition accuracy of the proposed method was 3.81 percentage points and 6.62 percentage points higher than that of the traditional CNN method. Experimental results on two databases showed that the proposed method was superior to the other four methods in both classification accuracy and Kappa coefficient, and it was a better classification method for hyperspectral remote sensing data classification. © 2019, Chinese Society of Agricultural Machinery. All right reserved.