Three dimensional Deep Learning approach for remote sensing image classification

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.

References in corpus (3)

• Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition
• Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
• Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

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• Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
• A Fast 3D CNN for Hyperspectral Image Classification
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• Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification
• Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification
• Compressive spectral image classification using 3D coded convolutional neural network
• Programmable Spectrometry -- Per-pixel Classification of Materials using Learned Spectral Filters
• A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification
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• MixerSENet: A Lightweight Framework for Efficient Hyperspectral Image Classification
• Hyperspectral Image Classification using Spectral-Spatial Mixer Network
• MixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification