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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 201–210 of 286 papers

TitleStatusHype
Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification—0
Multiview Transformer: Rethinking Spatial Information in Hyperspectral Image Classification—0
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification—0
Naive Gabor Networks for Hyperspectral Image Classification—0
Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classification—0
On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification—0
1D-Convolutional Capsule Network for Hyperspectral Image Classification—0
Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification—0
Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification—0
Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification—0
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