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HYPERSPECTRAL IMAGES CLASSIFICATION BASED ON FUSION FEATURES DERIVED FROM 1D AND 2D CONVOLUTIONAL NEURAL NETWORK
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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
doi:10.5194/isprs-archives-xlii-3-w10-335-2020
fatcat:jwjnknr46ffuxfjbwv5wsrzf7m