S- and X-band SAR data fusion

This paper investigates the benefits deriving from introducing a wavelet-transform-based fusion framework for multi-frequency Synthetic Aperture Radar (SAR) data. A specific application is considered in the assessment of the fused classification map derived and this is the discrimination of different kinds of oil in sea. S-band and X-band datasets, concurrently acquired from the same airborne platform, have here been used. The findings suggest that fusing S-band and X-band SAR data does improve the oil type discrimination between crude oil and diesel oil used in the exercise, although a more quantitative analysis should be conducted in the future to measure the degree of improvement

Oil spill detection using optical sensors: a multi-temporal approach

Oil pollution is one of the most destructive consequences due to human activities in the marine environment. Oil wastes come from many sources and take decades to be disposed of. Satellite based remote sensing systems can be implemented into a surveillance and monitoring network. In this study, a multi-temporal approach to the oil spill detection problem is investigated. Change Detection (CD) analysis was applied to MODIS/Terra and Aqua and OLI/Landsat 8 images of several reported oil spill events, characterized by different geographic location, sea conditions, source and extension of the spill. Toward the development of an automatic detection algorithm, a Change Vector Analysis (CVA) technique was implemented to carry out the comparison between the current image of the area of interest and a dataset of reference image, statistically analyzed to reduce the sea spectral variability between different dates. The proposed approach highlights the optical sensors’ capabilities in detecting oil spills at sea. The effectiveness of different sensors’ resolution towards the detection of spills of different size, and the relevance of the sensors’ revisiting time to track and monitor the evolution of the event is also investigated

Offshore platform sourced pollution monitoring using space-borne fully polarimetric C and X band synthetic aperture radar

Use of polarimetric SAR data for offshore pollution monitoring is relatively newand shows great potential for operational offshore platformmonitoring. This paper describes the development of an automated oil spill detection chain for operational purposes based on C-band (RADARSAT-2) and X-band (TerraSAR-X) fully polarimetric images, wherein we use polarimetric features to characterize oil spills and look-alikes. Numbers of near coincident TerraSAR-X and RADARSAT-2 images have been acquired over offshore platforms. Ten polarimetric feature parameterswere extracted fromdifferent types of oil and ‘look-alike’ spots and divided into training and validation dataset. Extracted features were then used to develop a pixel based Artificial Neural Network classifier. Mutual information contents among extracted features were assessed and feature parameters were ranked according to their ability to discriminate between oil spill and look-alike spots. Polarimetric features such as Scattering Diversity, Surface Scattering Fraction and Span proved to be most suitable for operational services

Iz stranih časopisa

U tekstu je dan popis radova koji su objavljeni u stranim časopisima

Iz stranih časopisa

U tekstu je dan popis radova koji su objavljeni u stranim časopisima

Comparative detections of oil spill using multimode radarsat-1 synthetic aperture radar satellite data

Oil spill or leakage into waterways and ocean spreads very rapidly due to the action of wind and currents. The study of the behavior and movement of these oil spills in sea had become imperative in describing a suitable management plan for mitigating the adverse impacts arising from such accidents. But the inherent difficulty of discriminating between oil spills and look-alikes is a main challenge with Synthetic Aperture Radar (SAR) satellite data and this is a drawback, which makes it difficult to develop a fully automated algorithm for detection of oil spill. As such, an automatic algorithm with a reliable confidence estimator of oil spill would be highly desirable. The main objective of this work is to develop comparative automatic detection procedures for oil spill pixels in multimode (Standard beam S2, Wide beam W1 and fine beam F1) RADARSAT-1 SAR satellite data that were acquired in the Malacca Straits using three algorithms namely, textures using cooccurrence matrix, post supervised classification, and neural network (NN) for oil spill detection with window size 7 x 7. The results show that the mean textures from co-occurrence matrix is the best indicator for oil spill detection as it can discriminate oil spill from its surrounding such as look-alikes, sea surface and land. The entropy and contrast textures can be mainly used for look-like detections. The receiver operator characteristic (ROC) was used to determine the accuracy of oil spill detection from RADARSAT-1 SAR data. The results show that oil spills, lookalikes, and sea surface roughness are perfectly discriminated with an area difference of 20% for oil spill, 35% look–alikes, 15% land and 30% for the sea roughness. The NN shows higher performance in automatic detection of oil spill in RADARSAT-1 SAR data as compared to other algorithms with standard deviation of 0.12. It can therefore be concluded that NN algorithm is an appropriate algorithm for oil spill automatic detection and W1 beam mode is appropriate for oil spill and look-alikes discrimination and detection

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations