A joint method of spatial–spectral features and BP neural network for hyperspectral image classification

Abstract

A hyperspectral image (HSI) has also highly correlated and redundant data, in addition to abundant spatial and spectral information. Only the spectral characteristics were usually utilized to perform the HSI classification in most previous studies, which leads to unsatisfactory accuracy and precision. The combination of Principal Component Analysis (PCA), Local Binary Pattern (LBP) and Back Propagation Neural Network (BPNN) (PCA-LBP-BPNN) was used to propose a novel classification method. More specifically, PCA was first used to reduce the dimensionality of HSIs for obtaining independent spectral bands sensitive to the classified objects. LBP was then adopted to extract the spatial texture features. Finally, the feature vectors were formed by fusing spatial–spectral features and input into BPNN for HSI classification. Three key parameters including the number of principal components (p) and the neurons in the hidden layer (l) as well as the learning rate (r) were optimally selected to improve the classification accuracy. Three publicly available hyperspectral datasets including Pavia University (PU), Salinas (Sa) and Botswana (Bo) were selected to validate the performance by comparing the kNN (k-Nearest Neighbor), SVM (Support Vector Machine) and Contextual Deep Convolutional Neural Network (CDCNN). The overall accuracy of PCA-LBP-BPNN reached 93.67%, 98.09% and 92.97%, respectively, for the three datasets. The method had a satisfying performance than kNN, SVM and CDCNN for the PU and Sa, but it had lower accuracies than kNN and CDCNN for the Bo due to extremely similar spectral responses. PCA-LBP-BPNN generally has satisfactory practicability and robustness in adapting to different hyperspectral datasets.

Keywords

• BP neural network
• Dimensionality reduction
• Hyperspectral remote sensing
• Local binary pat-tern
• Principal component analysis