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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
Transformers Fusion across Disjoint Samples for Hyperspectral Image Classification—0
Unsupervised Band Selection of Hyperspectral Images via Multi-dictionary Sparse Representation—0
Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification—0
WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification—0
Hyperspectral Image Classification and Clutter Detection via Multiple Structural Embeddings and Dimension Reductions—0
Hyperspectral Image Classification Based on Adaptive Sparse Deep Network—0
Hyperspectral Images Classification Based on Multi-scale Residual Network—0
Hyperspectral Image Classification Based on Sparse Modeling of Spectral Blocks—0
Hyperspectral Image Classification Based on Faster Residual Multi-branch Spiking Neural Network—0
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion—0
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