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 111–120 of 286 papers

TitleStatusHype
JigsawHSI: a network for Hyperspectral Image classificationCode1
A fast dynamic graph convolutional network and CNN parallel network for hyperspectral image classification—0
A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image ClassificationCode0
Hyperspectral Image Classification With Contrastive Graph Convolutional NetworkCode0
GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification—0
A 3-stage Spectral-spatial Method for Hyperspectral Image Classification—0
Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification—0
Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification—0
Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification—0
Deep Hyperspectral Unmixing using Transformer NetworkCode1
Show:102550
← PrevPage 12 of 29Next →

No leaderboard results yet.