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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 171–180 of 286 papers

TitleStatusHype
SLCRF: Subspace Learning with Conditional Random Field for Hyperspectral Image Classification—0
Predictive spectral analysis using an end-to-end deep model from hyperspectral images for high-throughput plant phenotyping—0
Compressive spectral image classification using 3D coded convolutional neural network—0
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image ClassificationCode1
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification—0
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion—0
Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning—0
Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification—0
Spatial--spectral FFPNet: Attention-Based Pyramid Network for Segmentation and Classification of Remote Sensing Images—0
Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification—0
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