HYPERSPECTRAL IMAGES CLASSIFICATION BASED ON FUSION FEATURES DERIVED FROM 1D AND 2D CONVOLUTIONAL NEURAL NETWORK

Abstract. In recent years, deep learning technology has been continuously developed and gradually transferred to various fields. Among them, Convolutional Neural Network (CNN), which has the ability to extract deep features of images due to its unique network structure, plays an increasingly important role in the realm of Hyperspectral images classification. This paper attempts to construct a features fusion model that combines the deep features derived from 1D-CNN and 2D-CNN, and explores the potential of features fusion model in the field of hyperspectral image classification. The experiment is based on the deep learning open source framework TensorFlow with Python3 as programming environment. Firstly, constructing multi-layer perceptron (MLP), 1D-CNN and 2DCNN models respectively, and then, using the pre-trained 1D-CNN and 2D-CNN models as feature extractors, finally, extracting features via constructing the features fusion model. The general open hyperspectral datasets (Pavia University) were selected as a test to compare classification accuracy and classification confidence among different models. The experimental results show that the features fusion model obtains higher overall accuracy (99.65%), Kappa coefficient (0.9953) and lower uncertainty for the boundary and unknown regions (3.43%) in the data set. Since features fusion model inherits the structural characteristics of 1D-CNN and 2DCNN, the complementary advantages between the models are achieved. The spectral and spatial features of hyperspectral images are fully exploited, thus getting state-of-the-art classification accuracy and generalization performance.