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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 171–180 of 286 papers

TitleStatusHype
TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification—0
A Supervised Segmentation Network for Hyperspectral Image Classification—0
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNNCode0
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image ClassificationCode0
WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification—0
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks—0
Hyperspectral classification of blood-like substances using machine learning methods combined with genetic algorithms in transductive and inductive scenarios—0
LiteDepthwiseNet: An Extreme Lightweight Network for Hyperspectral Image Classification—0
SLCRF: Subspace Learning with Conditional Random Field for Hyperspectral Image Classification—0
Predictive spectral analysis using an end-to-end deep model from hyperspectral images for high-throughput plant phenotyping—0
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