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Monitoring and identification of marine oil spills using advanced synthetic aperture radar images

Mihoub Z, Hassini A.

Optica Applicata

2014

Vol. 44, nr 3

433--449

The aim of this study is to propose and test a new methodology for detection of oil spills in the world oceans from advanced synthetic aperture radar imagery embedded in ENVISAT satellite (ENVISAT-ASAR). The proposed and applied methodology includes four levels: data acquisition, dark spots detection, features extraction and dark spots classification for discrimination between oil spills and look-alikes. Level 1 contains the ENVISAT-ASAR wide swath mode data acquisition. Level 2 begins with a visual interpretation based on experience and a priori information concerning location, external information about weather conditions, differences in shape, and contrast to surroundings between oil spills and look-alikes, then filtering and segmentation. Level 3 contains extraction of features from the detected dark spots. Level 4 aim is to discriminate oil spills from look-alikes using the features extracted by means of object-based fuzzy classification. As a result, oil slicks are discriminated from look-alikes with an overall accuracy classification of 91% for oil slicks and 86% for look-alikes. Finally, to validate our results, the method has been tested by comparing the areas of the automatically detected oil spills (object-based fuzzy classification) with the areas of the manually detected oil spills (region of interest), by means of area ratios.

Forest fires smoke monitoring from Sea-viewing Wide Field-of-view Sensor images

Hassini A., Dejean S., Benabadji N., Hassini N., Belbachir A. H.

Optica Applicata

2008

Vol. 38, nr 4

737-754

A method for detecting forest fires smoke using SeaWiFS (Sea-viewing Wide Field-of View Sensor) images is developed in this paper. The colour masking technique is proposed to extract the maximum fires smoke pixels from the SeaStar/SeaWiFS satellite images by using Fusion by Arithmetic Combination (FAC) of the spectral bands method. Each image used is converted from RGB (Red, Green, Blue) to HIS (Hue, Saturation, Intensity) system. The resulting smoke plumes pixels are obtained visually in the Intensity and Saturation images. Then the values of intensity and saturation are analyzed to be potentially applied in other images. In this research, we applied our detecting forest fires smoke algorithm in seven different scenes, and in a variety of conditions, including different regions of the planet, and different dates. Next, Smoke Pixel Reference Ratio (SPRR) was used to test the proposed method. We found that the method can detect maximum pixels of smoke plumes in spite of some limitations.