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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 191–200 of 286 papers

TitleStatusHype
Multimodal Hyperspectral Image Classification via Interconnected Fusion—0
A Supervised Segmentation Network for Hyperspectral Image Classification—0
Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification—0
A 3D 2D convolutional Neural Network Model for Hyperspectral Image Classification—0
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification—0
MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification—0
Multi-Scale U-Shape MLP for Hyperspectral Image Classification—0
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification—0
Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection—0
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