Integration of Spectral Histogram and Level Set for Coastline Detection in SAR Images

Modava, Mohammad; Akbarizadeh, Gholamreza; Soroosh, Mohammad

doi:10.1109/taes.2018.2865120

Cited by 82 publications

(28 citation statements)

References 34 publications

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“…Although it seems to be forcible, it is rather highly efficient, as long as there are abundant image samples containing land used for network training and learning. (2) In addition, the coastlines of the Earth are constantly changing, so the use of fixed coastline data from geo information to perform sea-land segmentation will inevitably result in some deviations [28], and especially, in fact, it is also challenging to obtain an accurate result of land-sea segmentation that are under extensive research by many other scholars [94][95][96]. (3) Today, deep learning-based SAR ship detectors always simultaneously locate many ships in large SAR images, instead of just ship-background binary classification in some small single chips, so the ship discrimination process is integrated into the end-to-end mode, improving detection efficiency.…”

Section: Advantage 3: Abundant Pure Backgroundsmentioning

confidence: 99%

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

Zhang

et al. 2020

Remote Sensing

163

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Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology. LS-SSDD-v1.0 is available at https://github.com/TianwenZhang0825/LS-SSDD-v1.0-OPEN.

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Section: Advantage 3: Abundant Pure Backgroundsmentioning

confidence: 99%

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

Zhang

et al. 2020

Remote Sensing

163

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“…It is important de mention that there are other approaches pursued to extract edges combining cellular automata and fuzzy rules [21] or local spectral histogram (LSH) [22] which are mainly applied on Synthetic Aperture Radar (SAR) images but that can be adapted to LV images' segmentation due to the high degree of accuracy in contour delineation and noise processing.…”

Section: Introductionmentioning

confidence: 99%

Left ventricular segmentation based on a parallel watershed transformation towards an accurate heart function evaluation

Mahmoudi

Ameur

Ammari

et al. 2020

IET Image Processing

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Magnetic Resonance Imaging (MRI) has emerged as the golden reference for cardiac examination. This modality allows the assessment of human cardiovascular morphology, functioning, and perfusion. Although a couple of challenging issues, such as the cardiac MR image's features and the large variability of images among several patients, still influences the cardiac cavities' segmentation and needs to be carried out. In this paper, we have profoundly reviewed and fully compared semi-automated segmentation methods performed on cardiac Cine-MR short-axis images for the evaluation of the left ventricular functions. However, the number of parameters handled by the synthesized works is limited if not null. For the sake of ensuring the highest coverage of the LV parameters computing, we have introduced a parallel watershedbased approach to segment the left ventricular allowing hence the computation of six parameters (End-Diastolic Volume, End-Systolic Volume, Ejection Fraction, Cardiac output, Stroke Volume and Left Ventricular Mass). An algorithm is associated with main considered measurements. The experimental results that were obtained through studying twenty patients' MRI data base, demonstrate the accuracy of our approach for estimating real values of the maximal set of parameters thanks to a faithful segmentation of the myocardium.

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“…erefore, an algorithm based on wavelet transform and support vector machine was proposed for texture recognition of SAR images [18]. Image processing technology has important applications in image registration [19], coastline detection [20,21], and so on. erefore, studying the MTS for highdimensional small sample data not only provides new ideas for dimension reduction and classification of small sample problems but also extends the application range of the MTS so that the MTS can also play a role in intelligent traffic system [22], image processing, machine vision, and other techniques of electronics field.…”

Section: Introductionmentioning

confidence: 99%

Optimized Mahalanobis–Taguchi System for High-Dimensional Small Sample Data Classification

Xiao

Shi

et al. 2020

Computational Intelligence and Neuroscience

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The Mahalanobis–Taguchi system (MTS) is a multivariate data diagnosis and prediction technology, which is widely used to optimize large sample data or unbalanced data, but it is rarely used for high-dimensional small sample data. In this paper, the optimized MTS for the classification of high-dimensional small sample data is discussed from two aspects, namely, the inverse matrix instability of the covariance matrix and the instability of feature selection. Firstly, based on regularization and smoothing techniques, this paper proposes a modified Mahalanobis metric to calculate the Mahalanobis distance, which is aimed at reducing the influence of the inverse matrix instability under small sample conditions. Secondly, the minimum redundancy-maximum relevance (mRMR) algorithm is introduced into the MTS for the instability problem of feature selection. By using the mRMR algorithm and signal-to-noise ratio (SNR), a two-stage feature selection method is proposed: the mRMR algorithm is first used to remove noise and redundant variables; the orthogonal table and SNR are then used to screen the combination of variables that make great contribution to classification. Then, the feasibility and simplicity of the optimized MTS are shown in five datasets from the UCI database. The Mahalanobis distance based on regularization and smoothing techniques (RS-MD) is more robust than the traditional Mahalanobis distance. The two-stage feature selection method improves the effectiveness of feature selection for MTS. Finally, the optimized MTS is applied to email classification of the Spambase dataset. The results show that the optimized MTS outperforms the classical MTS and the other 3 machine learning algorithms.

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Integration of Spectral Histogram and Level Set for Coastline Detection in SAR Images

Cited by 82 publications

References 34 publications

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

Left ventricular segmentation based on a parallel watershed transformation towards an accurate heart function evaluation

Optimized Mahalanobis–Taguchi System for High-Dimensional Small Sample Data Classification

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