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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 221–230 of 286 papers

TitleStatusHype
Convolution Based Spectral Partitioning Architecture for Hyperspectral Image ClassificationCode0
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNNCode0
Superpixelwise Low-rank Approximation based Partial Label Learning for Hyperspectral Image ClassificationCode0
Optimizing CNN-based Hyperspectral ImageClassification on FPGAsCode0
3D CNN with Localized Residual Connections for Hyperspectral Image ClassificationCode0
PCA-domain Fused Singular Spectral Analysis for fast and Noise-Robust Spectral-Spatial Feature Mining in Hyperspectral ClassificationCode0
Hyperspectral Image Classification in the Presence of Noisy LabelsCode0
Spatial-Geometry Enhanced 3D Dynamic Snake Convolutional Neural Network for Hyperspectral Image ClassificationCode0
When Segmentation Meets Hyperspectral Image: New Paradigm for Hyperspectral Image ClassificationCode0
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral ImagesCode0
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