Three dimensional Deep Learning approach for remote sensing image classification

TL;DR

This paper introduces a novel 3D deep learning method for remote sensing hyperspectral image classification, effectively combining spectral and spatial data to improve accuracy and efficiency over existing approaches.

Contribution

The paper proposes a new 3D deep learning architecture tailored for hyperspectral data, demonstrating superior classification performance and reduced computational costs.

Findings

Achieves higher classification accuracy than state-of-the-art methods

Reduces computational costs compared to existing approaches

Effectively integrates spectral and spatial information in hyperspectral images

Abstract

Recently, a variety of approaches has been enriching the field of Remote Sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited faced to the rich spatio-spectral content of today's large datasets. It would seem intriguing to resort to Deep Learning (DL) based approaches at this stage with regards to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS datasets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is firstly to explore the performance of DL architectures for the RS hyperspectral dataset classification and secondly to introduce a new three-dimensional DL approach that enables a joint spectral and spatial information process. A set of three-dimensional schemes is proposed and evaluated. Experimental results based on well knownhyperspectral datasets demonstrate that the proposed method is able to achieve a better classification rate than state of the art methods with lower computational costs.