Partially Supervised Oil-Slick Detection by SAR Imagery Using Kernel Expansion

Abstract

Abstract-Spaceborne synthetic aperture radar (SAR) is well adapted to detect ocean pollution independently from daily or weather conditions. In fact, oil slicks have a specific impact on ocean wave spectra. Initial wave spectra may be characterized by three kinds of waves, namely big, medium, and small, which correspond physically to gravity and gravity-capillary waves. The increase of viscosity, due to the presence of oil damps gravitycapillary waves. This induces not only a damping of the backscattering to the sensor but also a damping of the energy of the wave spectra. Thus, local segmentation of wave spectra may be achieved by the segmentation of a multiscale decomposition of the original SAR image. In this paper, a semisupervised oil-slick detection is proposed by using a kernel-based abnormal detection into the wavelet decomposition of a SAR image. It performs accurate detection with no consideration to signal stationarity nor to the presence of strong backscatters (such as a ship). The algorithm has been applied on ENVISAT Advanced SAR images. It yields accurate segmentation results even for small slicks, with a very limited number of false alarms.

Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach

“…The following alternative methods were used in the comparison: (a) Method A: our proposed approach is used without fast non-local means filtering; (b) Method B: our proposed approach is used without 2D-SWT and measurement level fusion-the approach utilized mathematical morphology and the EM algorithm for classification; (c) Method C: our proposed approach is used without morphological filtering; (d) Method D: our proposed approach is used without the EM algorithm-the approach employed the thresholding algorithm proposed by [32] and majority voting rule fusion for classification; (e) Method E: semi-supervised change detection, based on using a kernel-based abnormal detection into the wavelet decomposition of the SAR image [33]; (f) Method F: image denoising using fast discrete curvelet transform via wrapping with the EM algorithm to produce the change detection map [34]; (g) Method G: using UDWT to obtain a multiresolution representation of the log-ratio image, then identifying the number of reliable scales, and producing the final change detection map using fusion at feature level (FFL_ARS) on all reliable scales [15]; (h) Method H: implementing probabilistic Bayesian inferencing with the EM algorithm to perform unsupervised thresholding over the images generated by the dual-tree complex wavelet transform (DT-CWT) at various scales, and moreover, using intra-and inter-scale data fusion to produce the final change detection map [12]; (i) Method I: obtaining a multiresolution representation of the log-ratio image using UDWT, then applying the Chan-Vese (region-based) active contour model to the multiresolution representation to give the final change detection map [18]. Based on Table 2 and Figure 6, one can observe that the change detection result from Method G showed the lowest performance of all tested methods with an overall accuracy of 68.412% and a kappa coefficient of 0.162.…”

A Review of Kernel Methods in Remote Sensing Data Analysis

“…This method is a recent kernel-based development that only considers samples belonging to the class of interest in order to learn the underlying data class distribution. The method was originally introduced for anomaly detection [7], then analyzed for dealing with incomplete and unreliable training data [8], and recently reformulated for change detection [9].…”

A Novel Feature Derivation Technique for SVM based Hyper Spectral Image Classification

“…Camps-Valls proposes to estimate the local mean and variance for each pixel using fixed neighborhood [16]. Mercier proposes the same kernel formulation but the spatial information is estimated by wavelet decomposition of the image [17]. Mathieu Fauvel et.al. proposes the adaptive neighborhood of each pixel [18].Yuliya et.al.…”