Fully Automatic Dark-Spot Detection From SAR Imagery With the Combination of Nonadaptive Weibull Multiplicative Model and Pulse-Coupled Neural Networks

Taravat, Alireza; Latini, Daniele; Frate, Fabio Del

doi:10.1109/tgrs.2013.2261076

Cited by 40 publications

(24 citation statements)

References 38 publications

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“…The neurons establish inverse mapping, which discriminates the relations between inputs and outputs during the training phase. Several studies point out that ANNs are robust in terms of computational speed, stability and accuracy with respect to other investigated algorithms [36,37].…”

Section: Ann Model Descriptionmentioning

confidence: 99%

Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks

2019

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Grassland contributes to carbon storage and animal feed production. Its yield is largely determined by the cutting times of grassland. Previous studies have used remote sensing data for grassland biomass estimation, but only a few studies have focused on SAR remote sensing approaches for automatic grassland cutting status detection. Due to the occurrence of multiple cuttings in a year, it is crucial to effectively monitor grassland cutting events in order to achieve accurate biomass estimations of a whole season. In this study, we examined the capabilities of multilayer perceptron neural networks for automatic grassland cutting status detection using SAR imagery. The proposed model inputs are a time series dataset of VV and VH Sentinel-1 C-band SAR and second-order texture metrics (homogeneity, entropy, contrast and dissimilarity). The proposed approach has been successfully tested on a dataset collected from several fields in Germany in 2016, with an overall accuracy of 85.71% for the validation set.

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Section: Ann Model Descriptionmentioning

confidence: 99%

Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks

2019

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“…Traditionally, it has been assumed that the real and the imaginary parts of the received wave follow Gaussian distribution (Fernandes, 2001;Kuruoglu and Zerubia, 2004;Taravat et al, 2013). Another popular model is the Weibull distribution which has shown high degree of success in modeling urban scenes and sea clutter.…”

Section: Weibull Multiplicative Model (Wmm)mentioning

confidence: 99%

“…The proposed approach can be applied to the future spaceborne SAR images contains dark spots. Second, the sub-images are segmented by neural networks models (PCNN or MLP) (Brekke and Solberg, 2005;Taravat et al, 2013). As the last step, a very simple filtering process is used to eliminate the false targets.…”

Section: Introductionmentioning

confidence: 99%

Weibull Multiplicative Model and Machine Learning Models for Full-Automatic Dark-Spot Detection From Sar Images

Taravat

Frate

2013

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

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Abstract. As a major aspect of marine pollution, oil release into the sea has serious biological and environmental impacts. Among remote sensing systems (which is a tool that offers a non-destructive investigation method), synthetic aperture radar (SAR) can provide valuable synoptic information about the position and size of the oil spill due to its wide area coverage and day/night, and all-weather capabilities. In this paper we present a new automated method for oil-spill monitoring. A new approach is based on the combination of Weibull Multiplicative Model and machine learning techniques to differentiate between dark spots and the background. First, the filter created based on Weibull Multiplicative Model is applied to each sub-image. Second, the sub-image is segmented by two different neural networks techniques (Pulsed Coupled Neural Networks and Multilayer Perceptron Neural Networks). As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approaches were tested on 20 ENVISAT and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall dataset, the average accuracies of 94.05 % and 95.20 % were obtained for PCNN and MLP methods, respectively. The average computational time for dark-spot detection with a 256 × 256 image in about 4 s for PCNN segmentation using IDL software which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust and effective. The proposed approach can be applied to the future spaceborne SAR images.

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“…Taravat et al used a Weibull multiplication filter to suppress speckle noise, enhance the contrast between target and background, and used a multi-layer perceptron (MLP) neural network to segment the filtered SAR images [9]. Taravat et al also proposed a new method to distinguish dark spots from the combination of the Weibull multiplication model (WMM) and pulse coupled neural networks (PCNN) [10]. Singha used artificial neural networks (ANN) to identify the characteristics of oil slicks and lookalikes [11].…”

Section: Introductionmentioning

confidence: 99%

Dark Spot Detection in SAR Images of Oil Spill Using Segnet

Guo

2018

Applied Sciences

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Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However, some natural phenomena (such as waves, ocean currents, and low wind belts, as well as human factors) may change the backscatter intensity on the surface of the sea, resulting in uneven intensity, high noise, and blurred boundaries of oil slicks or lookalikes. In this paper, Segnet is used as a semantic segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of an oil spill. The effectiveness of the method is demonstrated through the comparison with fully convolutional networks (FCN), an initiator of semantic segmentation models, and some other segmentation methods. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show a higher robustness under high noise and fuzzy boundary conditions.

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Fully Automatic Dark-Spot Detection From SAR Imagery With the Combination of Nonadaptive Weibull Multiplicative Model and Pulse-Coupled Neural Networks

Cited by 40 publications

References 38 publications

Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks

Automatic Grassland Cutting Status Detection in the Context of Spatiotemporal Sentinel-1 Imagery Analysis and Artificial Neural Networks

Weibull Multiplicative Model and Machine Learning Models for Full-Automatic Dark-Spot Detection From Sar Images

Dark Spot Detection in SAR Images of Oil Spill Using Segnet

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