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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 131–140 of 286 papers

TitleStatusHype
Multimodal Hyperspectral Image Classification via Interconnected Fusion—0
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification—0
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive FilteringCode0
Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classification—0
Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification—0
Probabilistic Deep Metric Learning for Hyperspectral Image ClassificationCode0
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification—0
Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification—0
Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image ClassificationCode0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
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