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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 151–160 of 286 papers

TitleStatusHype
MSHCNet: Multi-Stream Hybridized Convolutional Networks with Mixed Statistics in Euclidean/Non-Euclidean Spaces and Its Application to Hyperspectral Image Classification—0
Multi-branch fusion network for hyperspectral image classification—0
Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions—0
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification—0
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification—0
Multimodal Hyperspectral Image Classification via Interconnected Fusion—0
Multiscale Convolutional Transformer with Center Mask Pretraining 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
Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection—0
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