SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–160 of 286 papers

TitleStatusHype
SpectralMamba: Efficient Mamba for Hyperspectral Image Classification—0
A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification—0
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings—0
Spectral Pyramid Graph Attention Network for Hyperspectral Image Classification—0
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—0
Hyperspectral Image Classification with Spatial Consistence Using Fully Convolutional Spatial Propagation Network—0
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance—0
Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field—0
Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis—0
Show:102550
← PrevPage 16 of 29Next →

No leaderboard results yet.