Hyperspectral Image Classification

104 papers with code • 8 benchmarks • 9 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 )

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Most implemented papers

Going Deeper with Contextual CNN for Hyperspectral Image Classification

eecn/Hyperspectral-Classification 12 Apr 2016

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 28 Feb 2018

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 17 Apr 2019

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 11 May 2019

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 17 Jun 2019

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 15 Jan 2021

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

mahmad00/Artifacts-of-DR-on-Hybrid-CNN-for-HSIC 25 Jan 2021

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

tanmay-ty/SpectralNET 1 Apr 2021

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 26 Jun 2021

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 7 Jul 2021

Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies.