Hyperspectral imaging classification based on convolutional neural networks by adaptive sizes of windows and filters

Abstract

Image classification by the convolutional neural network (CNN) has shown its great performances in recent years, in several areas, such as image processing and pattern recognition. However, there is still some improvement to do. The main problem in CNN is the initialisation of the number and size of the filters, which can obviously change the results. In this study, the authors assign three major contributions, based on the CNN model; (i) adaptive selection of the number of filters, (ii) using an adaptive size of the windows and (iii) using an adaptive size of the filters. The tests results, applied to different hyperspectral datasets (SalinasA, Pavia University, and Indian Pines), have proven that this framework is able to improve the accuracy of the hyperspectral image classification.