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
Going Deeper with Contextual CNN for Hyperspectral Image Classification
eecn/Hyperspectral-Classification
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The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map.
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image
eecn/Hyperspectral-Classification
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In this paper, we propose a novel convolutional neural network framework for the characteristics of hyperspectral image data, called HSI-CNN.
BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image
AngryCai/BS-Nets
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The framework consists of a band attention module (BAM), which aims to explicitly model the nonlinear inter-dependencies between spectral bands, and a reconstruction network (RecNet), which is used to restore the original HSI cube from the learned informative bands, resulting in a flexible architecture.
Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image Classification
stop68/remote-sensing-image-classification
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Deep learning models have achieved promising results on hyperspectral image classification, but their performance highly rely on sufficient labeled samples, which are scarce on hyperspectral images.
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images
ilyakava/pyfst
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Recent developments in machine learning and signal processing have resulted in many new techniques that are able to effectively capture the intrinsic yet complex properties of hyperspectral imagery.
Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
mahmad00/HSI-Traditional-to-Deep-Models
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Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images.
SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification
In this article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN for multi-resolution HSI classification.
Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification
dotwang/dcn-t
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To tackle these problems, in this paper, different from previous approaches, we perform the superpixel generation on intermediate features during network training to adaptively produce homogeneous regions, obtain graph structures, and further generate spatial descriptors, which are served as graph nodes.
SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers
danfenghong/IEEE_TGRS_SpectralFormer
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Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies.
