Hyperspectral Image Classification
92 papers with code • 8 benchmarks • 8 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Hyperspectral Image Classification
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Libraries
Use these libraries to find Hyperspectral Image Classification models and implementations
Indian Pines
Pavia University
Kennedy Space Center
Salinas
Houston
Botswana
WHU-Hi
LIB-HSI
Most implemented papers
DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification
dotwang/dcn-t
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Hyperspectral image (HSI) classification is challenging due to spatial variability caused by complex imaging conditions.
Fast forward feature selection for the nonlinear classification of hyperspectral images
Experimental results for two real hyperspectral data sets show that the method performs very well in terms of classification accuracy and processing time.
Deep supervised learning for hyperspectral data classification through convolutional neural networks
Our method exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task.
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Deep learning based landcover classification algorithms have recently been proposed in literature.
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
nshaud/DeepHyperX
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Remote Sensing 2017
Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification.
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
atelili/2bivqa
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In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it.
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework.
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost Tuning
We then demonstrate how this insight can be used to attain a good compromise between the rare and abundant classes without having to resort to cost set tuning, which has long been the norm for imbalanced classification.
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting
2) How is the performance of hyperspectral image classification methods affected when using disjoint train and test sets?
Hyperspectral image classification via a random patches network
Due to the remarkable achievements obtained by deep learning methods in the fields of computer vision, an increasing number of researches have been made to apply these powerful tools into hyperspectral image (HSI) classification.
