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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 121–130 of 286 papers

TitleStatusHype
Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification—0
Hyperspectral image classification using spectral-spatial LSTMs—0
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification—0
Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image Classification—0
Generating Hard Examples for Pixel-wise Classification—0
High Performance Hyperspectral Image Classification using Graphics Processing Units—0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification—0
Hyperspectral Images Classification Based on Multi-scale Residual Network—0
DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification—0
A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification—0
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