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DMAF-NET: Deep Multi-Scale Attention Fusion Network for Hyperspectral Image Classification with Limited Samples. [PDF]
Sensors (Basel)Guo H, Liu W.
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Ghostnet for Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2021Hyperspectral imaging (HSI) is a competitive remote sensing technique in several fields, from Earth observation to health, robotic vision, and quality control. Each HSI scene contains hundreds of (narrow) contiguous spectral bands. The amount of data generated by HSI devices is often both a solution and a problem for a given application.
Mercedes Eugenia Paoletti +4 more
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Hyperspectral image classification: A benchmark
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017Hyperspectral image classification, an astonishing tool to distinguish the land covers in remote sensed hyperspectral images, has been investigated by multiple disciplines such as geoscience, environmental science, mathematics, and computer vision.
Xudong Kang +2 more
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Hyperspectral Image Classification With Background
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Background (BKG) is an integral part of an image and has significant effect and impact on hyperspectral image classification (HSIC). Unfortunately, how to address the BKG issue has not received much attention over the past years. This paper investigates this issue by developing a mixed pixel classifier, iterative constrained energy minimization (ICEM ...
Xiao-Di Shang, Meiping Song, Chunyan Yu
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Boosting CNN for Hyperspectral Image Classification
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021In recent years, deep convolutional neural networks (CNNs) have been widely used for hyperspectral image (HSI) classification. Besides, ensemble learning is a useful way to enhance the classification performance. Therefore, in this study, a new method titled Boosting-CNN is proposed for HSI classification, which fully explored the advantages of deep ...
Haoyu Zhang +3 more
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Hyperspectral Image Classification With Adversarial Attack
IEEE Geoscience and Remote Sensing Letters, 2022The performance of a neural network is highly dependent on the labelled samples. However, the labelled samples are primarily clean, which prevents the network from capturing the features of the samples near the decision boundary. For hyperspectral images (HSIs), high-spectral dimensions and same-spectra foreign matter lead to more boundary samples in ...
Cheng Shi 0002 +4 more
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Adapting Kernels for Hyperspectral Image Classification
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021Despite its great potential in a wide range of human activities, hyperspectral remote sensing imaging (HSI) exhibits several challenges that prevent full exploitation of its data. In particular, land-cover classification based on HSI data suffers significant degradation due to problematic data variability.
Juan Mario Haut +7 more
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A probabilistic method for the classification of hyperspectral images
2016 24th Signal Processing and Communication Application Conference (SIU), 2016In this study a supervised classification and dimensionality reduction method for hyperspectral images is proposed. For this purpose, using probabilistic principal component analysis (PPCA), dimensionality reduction is performed and a Gaussian mixture model (GMM) is built.
Sezer Kutluk, Koray Kayabol, Aydin Akan
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Superpixel based classification of hyperspectral images
2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015Hyperspectral imaging captures a high number of spectrally narrow bands and provides advantages for image analysis applications such as identification and classification in particular. Hyperspectral images contain a large amount of bands. Processing these images causes the operation load substantially.
Cakmak, Mehtap +2 more
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Optimizing wavelets for hyperspectral image classification
2009 IEEE International Geoscience and Remote Sensing Symposium, 2009This work presents a procedure to optimize a wavelet filter in terms of discrimination capability between the classes characterizing a given hyperspectral remote sensing image. To this end, this procedure estimates the coefficients of the wavelet filter bank by means of a particle swarm optimization (PSO) so that to maximize the average Bhattacharyya ...
A. Daamouche, Melgani, Farid, L. Hamami
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