Spectral-spatial classification of hyperspectral images based on non-linear principal component analysis and deep learning models

Abstract

ABSTRACT Hyperspectral sensors, due to the acquisition of a large number of spectral bands, always have particular importance in monitoring the phenomena of the earth’s surface. The classification of hyperspectral images is the most crucial method of processing hyperspectral data, and many efforts have been made to increase its accuracy. In recent years, convolutional neural networks (CNNs) and spatial features have been a big part of how well hyperspectral images can be classified. CNNs, due to the automatic generation of features and reduction of parameters, compared to multilayer perceptron neural networks by sharing parameters in each layer, have received much attention from researchers in the field of pattern recognition. In previous research, much attention has not been paid to the simultaneous use of spatial feature extraction methods in CNNs. For this reason, in this paper, a new CNNs architecture has been introduced to classify hyperspectral images, which uses the spectral-spatial vector obtained from the morphological profiles method as the network’s input. Morphological profiles, which include a set of opening and closing filters, are generally applied to the components of the image that contain the most information. In this research, non-linear principal component analysis (non-linear PCA) implemented by neural networks was used to summarize hyperspectral image information. Therefore, the main innovation of this paper is to provide a three-stage framework for applying deep learning. The first step is to reduce the dimensions of the hyperspectral image using non-linear PCA, the second stage is to extract spatial features using the morphological profiles method, and the third step is to prepare the inputs and design the CNNs architecture. The proposed method was evaluated on three benchmark hyperspectral images of Pavia, Berlin and Telops. The results of the experiments show the superiority of the proposed method by 13, 11 and 17% in the overall accuracy parameter compared to the support vector machines (SVM) classification algorithm in the Pavia, Berlin and Telops images, respectively.