Hyperspectral remote sensing image classification using combinatorial optimisation based un‐supervised band selection and CNN

Abstract

Hyperspectral image (HSI) consists of hundreds of contiguous spectral bands, which can be used in the classification of different objects on the earth. The inclusion of both spectral as well as spatial features stands essential in order that high classification accuracy is achieved. However, incorporation of the spectral and spatial information without preserving the intrinsic structure of the data leads on to downscaling the classification accuracy. To address the issue aforementioned, the proposed method which involves using unsupervised spectral band selection based on three major constrains: (i) low reconstruction error with neighbourhood bands, (ii) low noise, (iii) high information entropy, is put forward. In addition, the structure‐preserving recursive filter is used to extract spatial features. Finally, the classification is performed using convolutional neural networks (CNNs) with different sets of convolutional, pooling, and fully connected layers. To test the performance of the proposed method, experiments have been carried out with three benchmark HSI datasets Indian pines, University of Pavia, and Salinas. These experiments reveal that the proposed method offers better classification accuracy over the purportedly state‐of‐the‐art methods in terms of standard metrics like overall accuracy, average accuracy, and kappa coefficient (K). The proposed method has attained OAs of 99.9, 98.9, and 99.93% for the three datasets, respectively.

Keywords

• convolutional neural networks
• benchmark HSI datasets Indian pines
• hyperspectral remote sensing image classification
• combinatorial optimisation
• un‐supervised band selection
• hyperspectral image