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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 251–260 of 286 papers

TitleStatusHype
ESW Edge-Weights : Ensemble Stochastic Watershed Edge-Weights for Hyperspectral Image Classification—0
Spatial-Aware Dictionary Learning for Hyperspectral Image Classification—0
Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification—0
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification—0
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification—0
Discrete Wavelet Transform-Based Capsule Network for Hyperspectral Image Classification—0
Spatial Context based Angular Information Preserving Projection for Hyperspectral Image Classification—0
Generating Hard Examples for Pixel-wise Classification—0
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering—0
Feature Selection and Classification of Hyperspectral Images With Support Vector Machines—0
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