Classification of Hyperspectral Images by Using Spectral Data and Fully Connected Neural Network

TL;DR

This paper demonstrates that hyperspectral images can be effectively classified using only spectral data and a fully connected neural network, achieving high accuracy without spatial convolution filters.

Contribution

The study introduces a novel approach of using solely spectral data with a fully connected neural network for hyperspectral image classification, bypassing the need for spatial convolution filters.

Findings

Achieved an average accuracy of 97.5% on multiple hyperspectral datasets.

Showed that spectral data alone can be sufficient for high-accuracy classification.

Challenged the common use of convolution filters on spatial data in hyperspectral image analysis.

Abstract

It is observed that high classification performance is achieved for one- and two-dimensional signals by using deep learning methods. In this context, most researchers have tried to classify hyperspectral images by using deep learning methods and classification success over 90% has been achieved for these images. Deep neural networks (DNN) actually consist of two parts: i) Convolutional neural network (CNN) and ii) fully connected neural network (FCNN). While CNN determines the features, FCNN is used in classification. In classification of the hyperspectral images, it is observed that almost all of the researchers used 2D or 3D convolution filters on the spatial data beside spectral data (features). It is convenient to use convolution filters on images or time signals. In hyperspectral images, each pixel is represented by a signature vector which consists of individual features that are independent of each other. Since the order of the features in the vector can be changed, it doesn't make sense to use convolution filters on these features as on time signals. At the same time, since the hyperspectral images do not have a textural structure, there is no need to use spatial data besides spectral data. In this study, hyperspectral images of Indian pines, Salinas, Pavia centre, Pavia university and Botswana are classified by using only fully connected neural network and the spectral data with one dimensional. An average accuracy of 97.5% is achieved for the test sets of all hyperspectral images.