Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra-Convolutional Neural Networks)

Abstract

Classification of Hyperspectral Satellite Images (HSI) is a very important technology for object detection and cartography. Several problems can be detected, which make classification difficult (large size of the images, fusion between the classes, small amount of samples, etc.). Recently, several Convolutional Neural Networks (CNN-HSI) have been proposed for the classification of hyperspectral images. In this article, an improvement to CNN-HSI is proposed, aiming to reduce the number of erroneous pixels during classification (due to the limited number of samples). Thus, an extra-convolution technique (ExCNN) is proposed, where we add layers of global convolutions on the classified images, outgoing from classical CNN. The addition of 1 to 10 layers, on three real hyperspectral images, is tested. The results obtained are compared with other similar methods of the state of art, and show the effectiveness of the proposed method.