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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 161–170 of 286 papers

TitleStatusHype
Multiview Transformer: Rethinking Spatial Information in 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
Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification—0
Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification—0
Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification—0
Predictive spectral analysis using an end-to-end deep model from hyperspectral images for high-throughput plant phenotyping—0
Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification—0
Randomized based restricted kernel machine for hyperspectral image classification—0
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