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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 61–70 of 286 papers

TitleStatusHype
3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image ClassificationCode0
SpectralMamba: Efficient Mamba for Hyperspectral Image Classification—0
Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification—0
A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image ClassificationCode0
Randomized Principal Component Analysis for Hyperspectral Image Classification—0
Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification—0
An Ultralightweight Hybrid CNN Based on Redundancy Removal for Hyperspectral Image Classification—0
Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image ClassificationCode0
HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis—0
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature EmbeddingCode0
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