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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 271–280 of 286 papers

TitleStatusHype
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach—0
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings—0
On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification—0
Going Deeper with Contextual CNN for Hyperspectral Image ClassificationCode0
Deep supervised learning for hyperspectral data classification through convolutional neural networksCode0
Hyperspectral Image Classification and Clutter Detection via Multiple Structural Embeddings and Dimension Reductions—0
Robust hyperspectral image classification with rejection fields—0
Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification—0
Task-Driven Dictionary Learning for Hyperspectral Image Classification with Structured Sparsity Constraints—0
Fast forward feature selection for the nonlinear classification of hyperspectral imagesCode0
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