Top 12 gan-based hyperspectral anomaly detection tuyệt nhất 2022

Tác giả: khủng long arxiv.org

Ngày đăng khủng long : 17/8/2021

Đánh giá: 5 ⭐ ( 64993 lượt đánh giá khủng long )

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Tóm tắt: khủng long In this paper, we propose a generative adversarial network (GAN)-basedhyperspectral anomaly detection algorithm. In the proposed algorithm, we traina GAN model to generate a synthetic background image which is close to theoriginal background image as much as possible. By subtracting the syntheticimage from the original one, we are able to remove the background from thehyperspectral image. Anomaly detection is performed by applying Reed-Xiaoli(RX) anomaly detector (AD) on the spectral difference image. In theexperimental part, we compare our proposed method with the classical RX,Weighted-RX (WRX) and support vector data description (SVDD)-based anomalydetectors and deep autoencoder anomaly detection (DAEAD) method on syntheticand real hyperspectral images. The detection results show that our proposedalgorithm outperforms the other methods in the benchmark.

Khớp với kết quả khủng long tìm kiếm: by S Arisoy · 2020 · Cited by 2 Abstract: In this paper, we propose a generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm.DOI: 10.23919/Eusipco47968.2020.9287675… xem ngay

Tác giả: khủng long ieeexplore.ieee.org

Ngày đăng khủng long : 13/2/2021

Đánh giá: 2 ⭐ ( 5514 lượt đánh giá khủng long )

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Tóm tắt: khủng long In this paper, we propose a generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm. In the proposed algorithm, we train a GAN model to generate a synthetic background image which is close to the original background image as much as possible. By subtracting the synthetic image from the original one, we are able to remove the background from the hyperspectral image. Anomaly detection is performed by applying Reed-Xiaoli (RX) anomaly detector (AD) on the spectral difference image. In the experimental part, we compare our proposed method with the classical RX, Weighted-RX (WRX) and support vector data description (SVDD)-based anomaly detectors and deep autoencoder anomaly detection (DAEAD) method on synthetic and real hyperspectral images. The detection results show that our proposed algorithm outperforms the other methods in the benchmark.

Khớp với kết quả khủng long tìm kiếm: by S Arisoy · 2021 · Cited by 2 Abstract: In this paper, we propose a generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm. In the proposed algorithm, …Date Added to IEEE Xplore: 18 December 2020Date of Conference: 18-21 Jan. 2021DOI: 10.23919/Eusipco47968.2020.9287675… xem ngay

Tác giả: khủng long www.semanticscholar.org

Ngày đăng khủng long : 17/6/2021

Đánh giá: 2 ⭐ ( 85059 lượt đánh giá khủng long )

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Tóm tắt: khủng long The proposed generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm is trained to generate a synthetic background image which is close to the original background image as much as possible and subtracting the synthetic image from the original one shows that the proposed algorithm outperforms the other methods in the benchmark. In this paper, we propose a generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm. In the proposed algorithm, we train a GAN model to generate a synthetic background image which is close to the original background image as much as possible. By subtracting the synthetic image from the original one, we are able to remove the background from the hyperspectral image. Anomaly detection is performed by applying Reed-Xiaoli (RX) anomaly detector (AD) on the spectral difference image. In the experimental part, we compare our proposed method with the classical RX, Weighted-RX (WRX) and support vector data description (SVDD)-based anomaly detectors and deep autoencoder anomaly detection (DAEAD) method on synthetic and real hyperspectral images. The detection results show that our proposed algorithm outperforms the other methods in the benchmark.

Khớp với kết quả khủng long tìm kiếm: Jul 5, 2020 The proposed generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm is trained to generate a synthetic …… xem ngay

Tác giả: khủng long openreview.net

Ngày đăng khủng long : 19/7/2021

Đánh giá: 2 ⭐ ( 53728 lượt đánh giá khủng long )

Đánh giá cao nhất: khủng long 5 ⭐

Đánh giá thấp nhất: khủng long 4 ⭐

Tóm tắt: khủng long Anomaly detection (AD) on hyperspectral images (HSIs) is of great importance in both space exploration and earth observations. However, the challenges caused by insufficient datasets, no labels…

Khớp với kết quả khủng long tìm kiếm: by T Jiang · 2020 Anomaly detection (AD) on hyperspectral images (HSIs) is of great importance in both space … based on ideas from the GAN and the sparse coding literature…. xem ngay

Tác giả: khủng long github.com

Ngày đăng khủng long : 17/6/2021

Đánh giá: 1 ⭐ ( 71645 lượt đánh giá khủng long )

Đánh giá cao nhất: khủng long 5 ⭐

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Tóm tắt: khủng long Applying generative adversarial networks for anomaly detection in hyperspectral remote sensing imagery – GitHub – awweide/pub-ffi-gan: Applying generative adversarial networks for anomaly detection…

Khớp với kết quả khủng long tìm kiếm: Applying generative adversarial networks for anomaly detection in hyperspectral remote sensing imagery – GitHub – awweide/pub-ffi-gan: Applying generative …… xem ngay

Tác giả: khủng long pubmed.ncbi.nlm.nih.gov

Ngày đăng khủng long : 4/6/2021

Đánh giá: 4 ⭐ ( 17629 lượt đánh giá khủng long )

Đánh giá cao nhất: khủng long 5 ⭐

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Tóm tắt: khủng long Anomaly detection (AD) using hyperspectral images (HSIs) is of great interest for deep space exploration and Earth observations. This article proposes a weakly supervised discriminative learning with a spectral constrained generative adversarial network (GAN) for hyperspectral anomaly detection (HAD …

Khớp với kết quả khủng long tìm kiếm: by T Jiang · Cited by 1 Anomaly detection (AD) using hyperspectral images (HSIs) is of … with a spectral constrained generative adversarial network (GAN) for …… xem ngay

Tác giả: khủng long www.spiedigitallibrary.org

Ngày đăng khủng long : 9/1/2021

Đánh giá: 5 ⭐ ( 56016 lượt đánh giá khủng long )

Đánh giá cao nhất: khủng long 5 ⭐

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Tóm tắt: khủng long The rising availability of hyperspectral data has increased the attention of anomaly detection for various applications. Anomaly detection aims to find a small number of pixels in the hyperspectral data for which the spectral signatures differ significantly from the background. However, for anomalies like camouflage objects in a rural area, the spectral signatures distinguish only by small features. For this purpose, we use a 1D-Convolutional Autoencoder, which extracts the background spectra’s most specific features to reconstruct the spectral signature by minimizing the loss function’s error. The difference between the original and the reconstructed data can be exploited for anomaly detection. Since the loss function is minimized based on predominant background spectra, areas with anomalies exhibit higher error values. The proposed anomaly detection method’s performance is tested on hyperspectral data in the range of 1000 to 2500 nm. The data was recorded with a drone-based Headwall sensor at approximately 80 m over a rural area near Greding, Germany. The anomalies consist mainly of camouflage materials and vehicles. We compare the performance of a 1D-Convolutional Autoencoder trained on a data set without the target anomalies for different models. This is done to quantify the number of anomalies in the data set before they inhibit the detection process. Additionally, the detection results are compared to the state-of-the-art Reed-Xiaoli anomaly detector. We present the results by counting the correct detections in relation to the false positives with the receiver operating characteristic and discuss more suitable evaluation approaches for small targets. We show that the 1D-CAE outperforms the Reed-Xiaoli anomaly detector for a false alarm rate of 0.1% by reconstructing the background with a low error and the anomalies with a higher error. The 1D-CAE is suitable for camouflage anomaly detection.

Khớp với kết quả khủng long tìm kiếm: by J Kuester · 2021 Since the loss function is minimized based on predominant background spectra, areas with anomalies exhibit higher error values. The proposed …… xem ngay

Tác giả: khủng long dblp.org

Ngày đăng khủng long : 12/3/2021

Đánh giá: 3 ⭐ ( 13793 lượt đánh giá khủng long )

Đánh giá cao nhất: khủng long 5 ⭐

Đánh giá thấp nhất: khủng long 4 ⭐

Tóm tắt: khủng long Bài viết về dblp: Weiying Xie. Đang cập nhật…

Khớp với kết quả khủng long tìm kiếm: Self-spectral learning with GAN based spectral-spatial target detection for … A Low-Complexity Hyperspectral Anomaly Detection Algorithm and Its FPGA …… xem ngay

Tác giả: khủng long paperswithcode.com

Ngày đăng khủng long : 9/3/2021

Đánh giá: 1 ⭐ ( 4619 lượt đánh giá khủng long )

Đánh giá cao nhất: khủng long 5 ⭐

Đánh giá thấp nhất: khủng long 1 ⭐

Tóm tắt: khủng long No code available yet.

Khớp với kết quả khủng long tìm kiếm: Jan 1, 2021 Hyperspectral anomaly detection under high-dimensional data and … methods based on generative adversarial network (GAN) suffer from the …… xem ngay

Tác giả: khủng long www.tandfonline.com

Ngày đăng khủng long : 16/1/2021

Đánh giá: 4 ⭐ ( 13134 lượt đánh giá khủng long )

Đánh giá cao nhất: khủng long 5 ⭐

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Tóm tắt: khủng long (2019). Hyperspectral anomaly detection using spectral–spatial features based on the human visual system. International Journal of Remote Sensing: Vol. 40, No. 23, pp. 8683-8704.

Khớp với kết quả khủng long tìm kiếm: by A Taghipour · 2019 · Cited by 11 In this paper, we propose a method that uses both spectral and spatial information of HSI based on human visual system (HVS). By inspiring the …… xem ngay