# Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification

Source: [Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification](<https://www.sota2.com/research/paper/68fa65f31e209131cea0d5a1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:39:29.972Z

Authors: Ringo S.W. Chu, Ho-Cheung Ng, Xiwei Wang, Wayne Luk

Published: 2019-06-27T22:06:15.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.11981>)

[PDF](<https://arxiv.org/pdf/1906.11981.pdf>)