Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier

Wang, Huili; Zhu, Ming; Lin, Chun-bo; Chen, Dian-bing

doi:10.1007/s11801-017-7014-9

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…The performance of binary segmentation using the PCDS algorithm is compared with the widely used Otsu thresholding algorithm [86] because thresholding algorithms are conventionally applied for binary segmentation of salient objects from grayscale maps [17,87]. Table 1 shows ten comparative image segmentation algorithms compared in this study to establish the performance of binary segmentation using the PCDS algorithm.…”

Section: Quantitative Evaluation Of Segmentation Resultsmentioning

confidence: 99%

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms.

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Section: Quantitative Evaluation Of Segmentation Resultsmentioning

confidence: 99%

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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“…Zhang et al2016Google Earthn/a0.12, 0.25−n/aDeep learning method: ship proposal extraction convolution neural networks−yesVessel detectionR. Zhang et al2016GaoFen-1, VRRS-1, Google EarthPAN2 (GF-1), 16 (VRSS-1)−20 pixelsDeep learning method: convolutional neural network, Singular value decomposition algorithmDiscrimination: SVM classifieryesVessel detectionZou and Shi2017Google Earthn/a−−n/aComputer vision method: rotation and scale-invariant method based on the pose consistency voting−yesInshore vessel detectionHe et al2017Google EarthPAN2−n/aSalient-based method: maximum symmetric surround method, cellular automata dynamic evolution model, Otsu algorithmDiscrimination: histogram of oriented gradient, AdaBoost classifieryesVessel detectionWang et al2017Google EarthB, G, R−−10 pixelsSalient-based method: combined saliency map model through a self-adaptive threshold based on Entropy informationDiscrimination: based on gradient featuresyesVessel detectionXu et al2017Google EarthPAN2−n/aSalient-based method: Histogram-based contrast method, phase spectrum of a Fourier transform, surface regular indexDiscrimination: Simple shape analysis, structure-local binary pattern, AdaBoost algorithmyesVessel detectionYang et al…”

Section: Inventory Of Evaluated Studiesmentioning

confidence: 99%

Vessel detection and classification from spaceborne optical images: A literature survey

Kanjir

Greidanus

Oštir

2018

Remote Sensing of Environment

234

170

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“…Earlier related works on this stage mainly used specially designed hand-crafted features to detect ships, including shape [21], texture [14,26], ship histogram of oriented gradient (S-HOG) [16,22], gist [27], structure local binary pattern (structure-LBP) [30], and different combinations of these features [14,17,29]. With these features, a classifier will be used to distinguish ships from false alarms, such as AdaBoost [31], support vector machine (SVM) [14,15], and extreme learning machine (ELM) [19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network

et al. 2020

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Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control. Most ship detection algorithms utilize a single-band image to design low-level and hand-crafted features, which are easily influenced by interference like clouds and strong waves and not robust for large-scale variation of ships. In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems. First, multi-spectral images are adopted for sea-land segmentation, and an enhanced DWT is employed to quickly extract ship candidate regions with missing alarms as low as possible. Second, panchromatic images with clear spatial details are used for ship classification. Specifically, we propose the local residual dense block (LRDB) to fully extract semantic feature via local residual connection and densely connected convolutional layers. DRDN mainly consists of four LRDBs and is designed to further remove false alarms. Furthermore, we exploit the multiclass classification strategy, which can overcome the large intra-class difference of targets and identify ships of different sizes. Extensive experiments demonstrate that the proposed method has high robustness in complex image backgrounds and achieves higher detection accuracy than other state-of-the-art methods.

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Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier

Cited by 17 publications

References 8 publications

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Vessel detection and classification from spaceborne optical images: A literature survey

A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network

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