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 221–230 of 286 papers

TitleStatusHype
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral ImagesCode0
Band Attention Convolutional Networks For Hyperspectral Image Classification—0
Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification—0
Segmentation-Aware Hyperspectral Image Classification—0
Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image ClassificationCode0
A novel statistical metric learning for hyperspectral image classification—0
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification—0
Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image ClassificationCode0
Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification—0
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification—0
Show:102550
← PrevPage 23 of 29Next →

No leaderboard results yet.