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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 11–20 of 286 papers

TitleStatusHype
Sparse Deformable Mamba for Hyperspectral Image Classification—0
Spatial-Geometry Enhanced 3D Dynamic Snake Convolutional Neural Network for Hyperspectral Image ClassificationCode0
Efficient Dynamic Attention 3D Convolution for Hyperspectral Image ClassificationCode0
Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image ClassificationCode0
M^3amba: CLIP-driven Mamba Model for Multi-modal Remote Sensing ClassificationCode1
Randomized based restricted kernel machine for hyperspectral image classification—0
Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction—0
Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification—0
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model—0
Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification—0
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