Effective supervised multiple‐feature learning for fused radar and optical data classification

Karimi, Danya; Akbarizadeh, Gholamreza; Rangzan, Kazem; Kabolizadeh, Mostafa

doi:10.1049/iet-rsn.2016.0346

Cited by 41 publications

(13 citation statements)

References 39 publications

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“…Hence, feature and decision level methods are suitable and more profitable for fusing radar and optical data. That's what has been achieved in the last two years (Karimi et al 2017).…”

Section: Figure 4: Types Of Combinations Of Satellite Images Used In mentioning

confidence: 75%

Fusing of Optical and Synthetic Aperture Radar (Sar) Remote Sensing Data: A Systematic Literature Review (Slr)

Mahyoub

Fadil

Mansour

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Remote sensing and image fusion have recognized many important improvements throughout the recent years, especially fusion of optical and synthetic aperture radar (SAR), there are so many published papers that worked on fusing optical and SAR data which used in many application fields in remote sensing such as Land use Mapping and monitoring. The goal of this survey paper is to summarize and synthesize the published articles from 2013 to 2018 which focused on the fusion of Optical and synthetic aperture radar (SAR) remote sensing data in a systematic literature review (SLR), based on the pre-published articles on indexed database related to this subject and outlining the latest techniques as well as the most used methods. In addition this paper highlights the most popular image fusion methods in this blending type. After conducting many researches in the indexed databases by using different key words related to the topic “fusion Optical and SAR in remote sensing”, among 705 articles, chosen 83 articles, which match our inclusion criteria and research questions as results ,all the systematic study ‘ questions have been answered and discussed.</p>

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“…Hence, feature and decision level methods are suitable and more profitable for fusing radar and optical data. That's what has been achieved in the last two years (Karimi et al 2017).…”

Section: Figure 4: Types Of Combinations Of Satellite Images Used In mentioning

confidence: 75%

Fusing of Optical and Synthetic Aperture Radar (Sar) Remote Sensing Data: A Systematic Literature Review (Slr)

Mahyoub

Fadil

Mansour

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Segmentation [5], [6], aim's is to delimit the image into segments which are conceptually meaningful such as the boundary between land and sea. Finally classification [7], [8], [9], [10] allows to label part of the images with regards to an application of interest. This paper focuses on target detection schemes and more specifically, on schemes which respect the Constant False Alarm (CFAR) property [11].…”

Section: A Motivationsmentioning

confidence: 99%

Design of New Wavelet Packets Adapted to High-Resolution SAR Images With an Application to Target Detection

Mian

Ovarlez

Atto

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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High-Resolution in synthetic aperture radar (SAR) leads to new physical characterizations of scatterers which are anisotropic and dispersive. These behaviors present an interesting source of diversity for target detection schemes. Unfortunately, such characteristics have been integrated and have been naturally lost in monovariate single look SAR images. Modeling this behavior as non-stationarity, wavelet analysis has been successful in retrieving this information. However, the sharp-edge of the used wavelet functions introduces undesired high side-lobes for the strong scatterers present in the images. In this paper, a new family of parametrized wavelets, designed specifically to reduce those side-lobes in the SAR image decomposition, is proposed. Target detection schemes are then explored using this spectroangular diversity and it can be shown that in High-Resolution SAR images, the non-Gaussian and robust framework leads to better results.

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“…Another recent method based on sparse representation and dictionary learning has also been developed in the literature and it was applied on CINE and Cardiac Phaseresolved Blood oxygen level-Dependent (CP-BOLD) MR sequences [14]. An interesting algorithm applied to the SAR images [15] based on the combination of random subspace (RS), linear discriminating analysis, sparse regularization (LDASR) for feature space dimensionality reduction, supervised feature selection, and learning was used for multi-sensor data fusion based on multiple features. The main advantage of this technique is the invariance under intensity changes.…”

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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Effective supervised multiple‐feature learning for fused radar and optical data classification

Cited by 41 publications

References 39 publications

Fusing of Optical and Synthetic Aperture Radar (Sar) Remote Sensing Data: A Systematic Literature Review (Slr)

Fusing of Optical and Synthetic Aperture Radar (Sar) Remote Sensing Data: A Systematic Literature Review (Slr)

Design of New Wavelet Packets Adapted to High-Resolution SAR Images With an Application to Target Detection

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

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