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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 101–110 of 286 papers

TitleStatusHype
DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification—0
Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification—0
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification—0
Does Normalization Methods Play a Role for Hyperspectral Image 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
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning—0
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification—0
Forward-Forward Algorithm for Hyperspectral Image Classification: A Preliminary Study—0
Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification—0
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