Hyperspectral Image Classification
97 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
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.
Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification
codeRimoe/DL_for_RSIs
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IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018
As it is very difficult and expensive to obtain class labels in real world, we integrate the proposed WCRN with AL to improve its generalization by using the most informative training samples.
Hyperspectral Image Classification in the Presence of Noisy Labels
The key idea of RLPA is to exploit knowledge (e. g., the superpixel based spectral-spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation.
Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification
With this architecture, the model gets a better performance and is more robust.
HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
This letter proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI classification.
Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data.
PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral images
In order to address these issues, we aim to develop a framework for material-agnostic information retrieval in hyperspectral images based on Positive-Unlabelled (PU) classification.
Deep Learning for Classification of Hyperspectral Data: A Comparative Review
nshaud/DeepHyperX
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IEEE Geoscience and Remote Sensing Magazine 2019
1 This article is intended for both data scientists with interest in hyperspectral data and remote sensing experts eager to apply deep learning techniques to their own dataset.
Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification
LEAP-WS/MDGCN
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To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information.
Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification
custom-computing-ic/SpecPatConv3D-Network
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Hyperspectral images (HSIs) can distinguish materials with high number of spectral bands, which is widely adopted in remote sensing applications and benefits in high accuracy land cover classifications.
