# Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification

## TL;DR

This paper introduces a 3D convolutional neural network architecture with spectral partitioning designed for hyperspectral image classification, effectively addressing high dimensionality and limited labeled data issues.

## Contribution

It proposes a novel deep learning architecture combining spectral partitioning with 3D CNNs for improved hyperspectral image classification.

## Key findings

- Achieves competitive classification accuracy on Indian Pines and Salinas datasets.
- Demonstrates robustness to high spectral dimensionality.
- Outperforms several existing methods in accuracy.

## Abstract

Hyperspectral images (HSIs) can distinguish materials with high number of spectral bands, which is widely adopted in remote sensing applications and benefits in high accuracy land cover classifications. However, HSIs processing are tangled with the problem of high dimensionality and limited amount of labelled data. To address these challenges, this paper proposes a deep learning architecture using three dimensional convolutional neural networks with spectral partitioning to perform effective feature extraction. We conduct experiments using Indian Pines and Salinas scenes acquired by NASA Airborne Visible/Infra-Red Imaging Spectrometer. In comparison to prior results, our architecture shows competitive performance for classification results over current methods.