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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 231–240 of 286 papers

TitleStatusHype
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks—0
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods—0
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification—0
WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification—0
Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification—0
Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification—0
3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification—0
Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification—0
SLCRF: Subspace Learning with Conditional Random Field for Hyperspectral Image Classification—0
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
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