Edge enhancement algorithm based on the wavelet transform for automatic edge detection in SAR images

This paper presents a novel technique for automatic edge enhancement and detection in synthetic aperture radar (SAR) images. The characteristics of SAR images justify the importance of an edge enhancement step prior to edge detection. Therefore, this paper presents a robust and unsupervised edge enhancement algorithm based on a combination of wavelet coefficients at different scales. The performance of the method is first tested on simulated images. Then, in order to complete the automatic detection chain, among the different options for the decision stage, the use of geodesic active contour is proposed. The second part of this paper suggests the extraction of the coastline in SAR images as a particular case of edge detection. Hence, after highlighting its practical interest, the technique that is theoretically presented in the first part of this paper is applied to real scenarios. Finally, the chances of its operational capability are assessed.Peer ReviewedPostprint (published version

Preparing levels 3 and 4 for the SMOS mission

Peer Reviewe

Investigating SAR algorithm for spaceborne interferometric oil spill detection

The environmental damages and recovery of terrestrial ecosystems from oil spills can last decades. Oil spills have been responsible for loss of aquamarine lives, organisms, trees, vegetation, birds and wildlife. Although there are several methods through which oil spills can be detected, it can be argued that remote sensing via the use of spaceborne platforms provides enormous benefits. This paper will provide more efficient means and methods that can assist in improving oil spill responses. The objective of this research is to develop a signal processing algorithm that can be used for detecting oil spills using spaceborne SAR interferometry (InSAR) data. To this end, a pendulum formation of multistatic smallSAR carrying platforms in a near equatorial orbit is described. The characteristic parameters such as the effects of incidence angles on radar backscatter, which support the detection of oil spills, will be the main drivers for determining the relative positions of the small satellites in formation. The orbit design and baseline distances between each spaceborne SAR platform will also be discussed. Furthermore, results from previous analysis on coverage assessment and revisit time shall be highlighted. Finally, an evaluation of automatic algorithm techniques for oil spill detection in SAR images will be conducted and results presented. The framework for the automatic algorithm considered consists of three major steps. The segmentation stage, where techniques that suggest the use of thresholding for dark spot segmentation within the captured InSAR image scene is conducted. The feature extraction stage involves the geometry and shape of the segmented region where elongation of the oil slick is considered an important feature and a function of the width and the length of the oil slick. For the classification stage, where the major objective is to distinguish oil spills from look-alikes, a Mahalanobis classifier will be used to estimate the probability of the extracted features being oil spills. The validation process of the algorithm will be conducted by using NASA’s UAVSAR data obtained over the Gulf of coast oil spill and RADARSAT-1 dat

shoreline detection capability of cosmo skymed and high resolution multispectral images

n/

Coastline changes monitoring using satellite images of Makassar Coastal Areas

Coastal areas have been the target of rapid urban development in most large cities in Indonesia. Makassar City, the capital city of the South Sulawesi Province, is undeniably a fast growing city both economically and spatially. Especially the western coast of this city is rapidly growing in the form of coastal reclamation to accommodate the extension of settlement. This situation will continue in association with the social needs pursuing development to have better living conditions. As the number of population increases, industries have been triggered to provide new open areas for business and construction, and this situation inevitably demands spaces. Unfortunately the purposes of spatial planning of the city have not always been followed by the actual situations of spatial development. The coastline has changed drastically, with concentrated changes being observed along the coastline of Makassar beach such as at Tanjung Bayam and Losari Beach. In this study, we use Landsat satellite images acquired from 1990 to 2010 and Ikonos high-resolution, time-series images to monitor the coastline changes of the city. The interpretation is validated with the ground observations in survey campaigns conducted in some of the target areas. The temporal changes seen in the satellite time series are evaluated using newly developed software appropriate for raster images. The result of the present study strongly suggests that the human activity and coastal physical aspects are influencing the geomorphological changes in this city especially along the coastline of the city. This study is expected to contribute in the future city planning of Makassar when considering which areas to be developed for what purposes with better prioritization

an application of cosmo sky med to coastal erosion studies

AbstractStarted in 2009, the COSMOCoast project aims to the investigation of the potential of Remote Sensing in support to the management of coastal areas. Particular attention is paid to the contribution of data acquired from the COSMO-SkyMed constellation, in view of their frequency of acquisitions and ground resolution; in particular this paper aims at assessing the potential of COSMO-SkyMed data for coastline delineation. The results are conceived to be of particular interest for public administration bodies in charge of coastal defense. Keywords: Remote Sensing, Coastal Zones Management, COSMO-SkyMed

HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we therefore devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at \url{https://github.com/khdlr/HED-UNet}.Comment: This work has been accepted by IEEE TGRS for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl