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Hyperspectral Image Classification

Hyperspectral Image Classification is a task in the field of remote sensing and computer vision. It involves the classification of pixels in hyperspectral images into different classes based on their spectral signature. Hyperspectral images contain information about the reflectance of objects in hundreds of narrow, contiguous wavelength bands, making them useful for a wide range of applications, including mineral mapping, vegetation analysis, and urban land-use mapping. The goal of this task is to accurately identify and classify different types of objects in the image, such as soil, vegetation, water, and buildings, based on their spectral properties.

( Image credit: Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification )

Papers

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Showing 191–200 of 286 papers

TitleStatusHype
A Fast 3D CNN for Hyperspectral Image ClassificationCode1
Hyperspectral Images Classification Based on Multi-scale Residual Network—0
LiteDenseNet: A Lightweight Network for Hyperspectral Image Classification—0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification—0
Improving Deep Hyperspectral Image Classification Performance with Spectral Unmixing—0
Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)Code1
Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network—0
A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme—0
Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification—0
Statistical Loss and Analysis for Deep Learning in Hyperspectral Image ClassificationCode0
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