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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 131–140 of 286 papers

TitleStatusHype
Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image ClassificationCode0
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature EmbeddingCode0
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive FilteringCode0
High Performance Hyperspectral Image Classification using Graphics Processing Units—0
A novel statistical metric learning for hyperspectral image classification—0
Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification—0
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification—0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
Compressive spectral image classification using 3D coded convolutional neural network—0
A fast dynamic graph convolutional network and CNN parallel network for hyperspectral image classification—0
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