LIME-Based Data Selection Method for SAR Images Generation Using GAN

Deep learning has obtained remarkable achievements in computer vision, especially image and video processing. However, in synthetic aperture radar (SAR) image recognition, the application of DNNs is usually restricted due to data insufficiency. To augment datasets, generative adversarial networks (G...
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Autor*in:	Mingzhe Zhu [verfasserIn] Bo Zang [verfasserIn] Linlin Ding [verfasserIn] Tao Lei [verfasserIn] Zhenpeng Feng [verfasserIn] Jingyuan Fan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	generative adversarial networks (GAN) synthetic aperture radar (SAR) deep neural network understanding target interpretation and recognition local interpretable model-agnostic explanation (LIME)

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 1, p 204
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:1, p 204

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14010204

Katalog-ID:	DOAJ01963093X

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author	Mingzhe Zhu
spellingShingle	Mingzhe Zhu misc generative adversarial networks (GAN) misc synthetic aperture radar (SAR) misc deep neural network understanding misc target interpretation and recognition misc local interpretable model-agnostic explanation (LIME) misc Science misc Q LIME-Based Data Selection Method for SAR Images Generation Using GAN
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topic_title	LIME-Based Data Selection Method for SAR Images Generation Using GAN generative adversarial networks (GAN) synthetic aperture radar (SAR) deep neural network understanding target interpretation and recognition local interpretable model-agnostic explanation (LIME)
topic	misc generative adversarial networks (GAN) misc synthetic aperture radar (SAR) misc deep neural network understanding misc target interpretation and recognition misc local interpretable model-agnostic explanation (LIME) misc Science misc Q
topic_unstemmed	misc generative adversarial networks (GAN) misc synthetic aperture radar (SAR) misc deep neural network understanding misc target interpretation and recognition misc local interpretable model-agnostic explanation (LIME) misc Science misc Q
topic_browse	misc generative adversarial networks (GAN) misc synthetic aperture radar (SAR) misc deep neural network understanding misc target interpretation and recognition misc local interpretable model-agnostic explanation (LIME) misc Science misc Q
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title	LIME-Based Data Selection Method for SAR Images Generation Using GAN
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title_full	LIME-Based Data Selection Method for SAR Images Generation Using GAN
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title_sort	lime-based data selection method for sar images generation using gan
title_auth	LIME-Based Data Selection Method for SAR Images Generation Using GAN
abstract	Deep learning has obtained remarkable achievements in computer vision, especially image and video processing. However, in synthetic aperture radar (SAR) image recognition, the application of DNNs is usually restricted due to data insufficiency. To augment datasets, generative adversarial networks (GANs) are usually used to generate numerous photo-realistic SAR images. Although there are many pixel-level metrics to measure GAN’s performance from the quality of generated SAR images, there are few measurements to evaluate whether the generated SAR images include the most representative features of the target. In this case, the classifier probably categorizes a SAR image into the corresponding class based on “wrong” criterion, i.e., “Clever Hans”. In this paper, local interpretable model-agnostic explanation (LIME) is innovatively utilized to evaluate whether a generated SAR image possessed the most representative features of a specific kind of target. Firstly, LIME is used to visualize positive contributions of the input SAR image to the correct prediction of the classifier. Subsequently, these representative SAR images can be selected handily by evaluating how much the positive contribution region matches the target. Experimental results demonstrate that the proposed method can ally “Clever Hans” phenomenon greatly caused by the spurious relationship between generated SAR images and the corresponding classes.
abstractGer	Deep learning has obtained remarkable achievements in computer vision, especially image and video processing. However, in synthetic aperture radar (SAR) image recognition, the application of DNNs is usually restricted due to data insufficiency. To augment datasets, generative adversarial networks (GANs) are usually used to generate numerous photo-realistic SAR images. Although there are many pixel-level metrics to measure GAN’s performance from the quality of generated SAR images, there are few measurements to evaluate whether the generated SAR images include the most representative features of the target. In this case, the classifier probably categorizes a SAR image into the corresponding class based on “wrong” criterion, i.e., “Clever Hans”. In this paper, local interpretable model-agnostic explanation (LIME) is innovatively utilized to evaluate whether a generated SAR image possessed the most representative features of a specific kind of target. Firstly, LIME is used to visualize positive contributions of the input SAR image to the correct prediction of the classifier. Subsequently, these representative SAR images can be selected handily by evaluating how much the positive contribution region matches the target. Experimental results demonstrate that the proposed method can ally “Clever Hans” phenomenon greatly caused by the spurious relationship between generated SAR images and the corresponding classes.
abstract_unstemmed	Deep learning has obtained remarkable achievements in computer vision, especially image and video processing. However, in synthetic aperture radar (SAR) image recognition, the application of DNNs is usually restricted due to data insufficiency. To augment datasets, generative adversarial networks (GANs) are usually used to generate numerous photo-realistic SAR images. Although there are many pixel-level metrics to measure GAN’s performance from the quality of generated SAR images, there are few measurements to evaluate whether the generated SAR images include the most representative features of the target. In this case, the classifier probably categorizes a SAR image into the corresponding class based on “wrong” criterion, i.e., “Clever Hans”. In this paper, local interpretable model-agnostic explanation (LIME) is innovatively utilized to evaluate whether a generated SAR image possessed the most representative features of a specific kind of target. Firstly, LIME is used to visualize positive contributions of the input SAR image to the correct prediction of the classifier. Subsequently, these representative SAR images can be selected handily by evaluating how much the positive contribution region matches the target. Experimental results demonstrate that the proposed method can ally “Clever Hans” phenomenon greatly caused by the spurious relationship between generated SAR images and the corresponding classes.
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title_short	LIME-Based Data Selection Method for SAR Images Generation Using GAN
url	https://doi.org/10.3390/rs14010204 https://doaj.org/article/81ae50b4fc334e0eb92359cdb1e2f68a https://www.mdpi.com/2072-4292/14/1/204 https://doaj.org/toc/2072-4292
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up_date	2024-07-04T00:20:34.212Z
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LIME-Based Data Selection Method for SAR Images Generation Using GAN

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