Ship Detection From Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature

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

“…A relatively large fraction of the authors (35%) used Google Earth images as the direct input data source in their research on vessel detection – either as exported data or simply as a print screen –, or as a source for collecting the greater amount of test data for machine learning methods (An et al, 2013, p. 201; Deng et al, 2013, Dong et al, 2013, Gan et al, 2015, Guo et al, 2015, Han et al, 2014, Hong et al, 2007, Huang et al, 2016, Johansson, 2011, Ju, 2015, Ma et al, 2010, p. 201; Shi et al, 2014, Xu et al, 2017, Xu et al, 2011, p. 201; Xu and Liu, 2016, Xu et al, 2014, Yang et al, 2017, Yang et al, 2014, You and Li, 2011, Zhang et al, 2016, Zou and Shi, 2016). One author has used data from Microsoft Virtual Earth (Yin et al, 2016).…”

“…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…”

A Method of Ship Detection under Complex Background

“…These results demonstrate the effectiveness and applicability of our method. To verify that the proposed method had a positive impact on ship detection, we compared our method with the state-of-the-art methods proposed in References [17,18] on Recall and Precision. These experiments were conducted on 290 images of the different situations above-mentioned, including the quiet sea, textured sea, and cluttered sea.…”

“…These experiments were conducted on 290 images of the different situations above-mentioned, including the quiet sea, textured sea, and cluttered sea. The Recall and Precision of the method used in Reference [17] were 87.65% and 80.47%, respectively, while the Recall and Precision of the method in Reference [18] were 80.08% and 78.92%, respectively. In contrast, our method achieved a detection effect with higher accuracy.…”

“…Reference [17] integrated intensity distinctness and the significant map into the process of ship detection. ROIs were obtained by dynamic threshold segmentation, and shape features and the structure LBP of ships were used to determine whether the candidate was a real ship.…”