Weighted image defogging method using statistical RGB channel feature extraction

Yoon, Inhye; Jeon, Jaehwan; Lee, Jin‐Hee; Paik, Joonki

doi:10.1109/socdc.2010.5682979

Cited by 16 publications

(5 citation statements)

References 4 publications

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“…Ship targets that are obscured by the fog will pose changes to the ship target classification algorithms, thus leading to drop in accuracy. Previous effort to alleviate the impact of fog occlusion aims to remove the fog component from images, such as the defogging method proposed by Yoon et al [30]. However, this method only uses the statistical color features and, therefore, is not able to cover the full spectrum of fog occlusion conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the contribution of the similarity constraint loss and center loss is validated. To test the target classification performance of the IICL-CNN model, we compared it with InceptionV3 [14], InceptionV4 [15], SVDNet [25], PCANet [26], defogging algorithm [30], Resnet50 [42], and a traditional method using SVM classifier and the histogram of oriented gradients (SVM + HOG) [43]. The test set contains 335 clear ship images and their simulated fog occluded images at 5 occlusion levels resulting in atotal number of 2010 samples.…”

Section: F Ship Target Classificationmentioning

confidence: 99%

“…One indirect solution to alleviate the impact of fog occlusion is to remove the fog component from images before feeding them to a CNN model. For example, Yoon et al [30] presented a weighted adaptive image defogging method by extracting features in the RGB channels. Yao et al [31]proposed a method to calculate the defogging rate from the effective edge strength, proving its effectiveness and robustness .…”

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confidence: 99%

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An Improved InceptionV3 Network for Obscured Ship Classification in Remote Sensing Images

Liu

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Ship target classification plays an important role in tasks such as maritime traffic control, maritime target tracking, and military reconnaissance. The complex ocean environment often causes obscuration of the ship targets, thus resulting in low accuracy of the obscured targets. This paper presents a novel target classification algorithm-Improved InceptionV3 and Center Loss CNN (IICL-CNN)-based on the well-established Inception network to improve the accuracy of obscured targets. This algorithm features a new objective function, which is designed to learn common features of both the clear samples and the obscured samples and, in the meantime, reduce the intra-class distance among the obscured samples. Experiments were performed on an optical remote sensing image dataset which consisted of 48,000 ship images in 9 categories. The proposed method demonstrated superior performance on the obscured ship targets compared to the original InceptionV3 model. On average, the accuracy was 4.23%, 5.98%, and 17.48% higher on the ship targets that were occluded by levels of 30%, 50%, and 70%, respectively. Our experimental results showed that the proposed IICL-CNN could effectively improve the accuracy of the ship targets at various occlusion levels. Index Terms-Convolution neural network, deep learning, feature extraction, fog occlusion, image classification, remote sensing image I. INTRODUCTION HIP target classification is an emerging research area which has a broad range of applications in civil and military fields, such as maritime traffic control, onshore search, maritime target tracking, and military reconnaissance. With the developments in computer vision and machine learning, there are many recent studies in this area. Zhu et al. [1] recently proposed a method for ship identification, which extracted multiple highdimensional local features from ship images and then used Support Vector Machine (SVM) for target classification. In order to identify ship targets more accurately, SVM is combined with other methods to improve effectiveness and robustness [2, 3, 4, 5]. Aiming at the sparseness of targets in optical remote sensing images, Li et al. [6] proposed a method for target detection and classification based on morphological

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Section: Discussionmentioning

confidence: 99%

Section: F Ship Target Classificationmentioning

confidence: 99%

mentioning

confidence: 99%

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An Improved InceptionV3 Network for Obscured Ship Classification in Remote Sensing Images

Liu

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…This is done using histogram specification to make an improvement on the image after dark channel prior is applied to the hazy image. Another adaptive method performs defogging by extracting features in the RGB color channels [40]. This weighted adaptive method overcomes the problem of local color distortion.…”

Section: Dark Channel Priormentioning

confidence: 99%

Benchmarking Single Image Dehazing Methods

Nair

Sankaran

2021

SN COMPUT. SCI.

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“…Soon afterwards, another unique method called the dark channel prior (DCP) by He et al [9] supported the ability to infer a raw estimate of t http://jivp.eurasipjournals.com/content/2013/1/37 using a single image with fog present. The DCP method has also influenced many more single image defogging methods (see [10][11][12][13][14][15][16]). Within the same time frame, Tarel and Hautière [17] introduced a fast single image defogging method that also estimates the transmission map.…”

Section: Introductionmentioning

confidence: 99%

An analysis of single image defogging methods using a color ellipsoid framework

Gibson

Nguyen

2013

J Image Video Proc

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The goal of this article is to explain how several single image defogging methods work using a color ellipsoid framework. The foundation of the framework is the atmospheric dichromatic model which is analogous to the reflectance dichromatic model. A key step in single image defogging is the ability to estimate relative depth. Therefore, properties of the color ellipsoids are tied to depth cues within an image. This framework is then extended using a Gaussian mixture model to account for multiple mixtures which gives intuition in more complex observation windows, such as observations at depth discontinuities which is a common problem in single image defogging. A few single image defogging methods are analyzed within this framework and surprisingly tied together with a common approach in using a dark prior. A new single image defogging method based on the color ellipsoid framework is introduced and compared to existing methods.

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Weighted image defogging method using statistical RGB channel feature extraction

Cited by 16 publications

References 4 publications

An Improved InceptionV3 Network for Obscured Ship Classification in Remote Sensing Images

An Improved InceptionV3 Network for Obscured Ship Classification in Remote Sensing Images

Benchmarking Single Image Dehazing Methods

An analysis of single image defogging methods using a color ellipsoid framework

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