Daniele Latini

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

Dark-spot detection is a critical step in oil-spill detection. In this paper, a novel approach for automated dark-spot detection using synthetic aperture radar imagery is presented. A new approach from the combination of Weibull multiplicative model (WMM) and pulse-coupled neural network (PCNN) techniques is proposed to differentiate between the dark spots and the background. First, the filter created based on WMM is applied to each subimage. Second, the subimage is segmented by PCNN techniques. As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approach was tested on 60 Envisat and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall data set, an average accuracy of 93.66% was obtained. The average computational time for dark-spot detection with a 512 × 512 image is about 7 s 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 on any kind of synthetic aperture radar imagery.

PCNN for automatic segmentation and information extraction from X-band SAR imagery

The extremely high number of synthetic aperture radar (SAR) images provided by the current spaceborne missions demand for the development of even more effective automatic techniques for data processing. In this context, neural approaches can give significant contributions being characterised by a high level of automatism. In particular, rather interesting potential is provided by the pulse-coupled neural networks (PCNNs), which have been designed with the idea of simulating the visual cortex of small mammals. In this article, the performance of PCNNs for automatic object extraction from satellite with very high-resolution SAR images is examined by applying them to different cases of interest.

An application of COSMO-Sky Med to coastal erosion studies

Abstract Started 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.

A novel multi-band SAR data technique for fully automatic oil spill detection in the ocean

With the launch of the Italian constellation of small satellites for the Mediterranean basin observation COSMO-SkyMed and the German TerraSAR-X missions, the delivery of very high-resolution SAR data to observe the Earth day or night has remarkably increased. In particular, also taking into account other ongoing missions such as Radarsat or those no longer working such as ALOS PALSAR, ERS-SAR and ENVISAT the amount of information, at different bands, available for users interested in oil spill analysis has become highly massive. Moreover, future SAR missions such as Sentinel-1 are scheduled for launch in the very next years while additional support can be provided by Uninhabited Aerial Vehicle (UAV) SAR systems. Considering the opportunity represented by all these missions, the challenge is to find suitable and adequate image processing multi-band procedures able to fully exploit the huge amount of data available. In this paper we present a new fast, robust and effective automated approach for oil-spill monitoring starting from data collected at different bands, polarizations and spatial resolutions. A combination of Weibull Multiplicative Model (WMM), Pulse Coupled Neural Network (PCNN) and Multi-Layer Perceptron (MLP) techniques is proposed for achieving the aforementioned goals. One of the most innovative ideas is to separate the dark spot detection process into two main steps, WMM enhancement and PCNN segmentation. The complete processing chain has been applied to a data set containing C-band (ERS-SAR, ENVISAT ASAR), X-band images (Cosmo-SkyMed and TerraSAR-X) and L-band images (UAVSAR) for an overall number of more than 200 images considered.

The Gulf of Mexico oil rig accident: analysis by different SAR satellite images

The management of the monitoring oil spills over the sea surface is a very important and actual task for international environmental agencies, due to the continuous risks represented by possible accidents involving either rigs or tankers. On the other hand the increase of remote sensing space missions can definitely improve our capabilities in this kind of activity. In this paper we consider the dramatic Gulf of Mexico oil spill event of 2010 to investigate on the types of information that could be provided by the available SAR images collection which included different polarizations and bands. With an eye to the implementation of fully automatic processing chains, an assessment of a novel segmentation technique based on PCNN (Pulse Coupled Neural Networks) was also carried out.

EO-based products in support of urban heat fluxes estimation

Presently, there is a growing need for information suitable to effectively characterize the Urban Energy Budget (UEB) and, hence, to properly estimate the magnitude of the anthropogenic heat flux QF. Indeed, a precise knowledge of QF - whose implications for urban planners are still prone to large uncertainties - is fundamental for implementing effective strategies to improve thermal comfort and energy efficiency. To address this challenging issue, the Horizon 2020 URBANFLUXES project aims at developing a novel methodology for accurately estimating the different terms of the UEB based on the use of Earth Observation (EO) data and, hence, at reliably characterizing the QF spatiotemporal patterns and its implications on urban climate. In this paper, we aim at giving an overview of the EO-based products which have been identified as the most useful in the framework of the considered study. In particular, the suite which has been implemented so far in the first phase of the project includes biophysical parameters, morphology parameters as well as land-cover maps.

A new automatic technique for coastline extraction from SAR images

The coastal marine habitat is an important and delicate environment from economical, ecological, political and security point of view, therefore its integrity has to be monitored and preserved from dangerous human activities. Recent studies have demonstrated that the 42% of the Italian Coast is eroding because of the increase of the sea-level height and the reduced solid transport from rivers to sea, hence there is an important requirement for tools capable to provide a synoptic view of the coastal area. COSMO-SkyMed SAR products with their very high resolution and short revisit time, can represent a breakthrough on coastline delineation and mapping, also overcoming the problems related to cloud cover or large extension of the areas. While in remotely sensed imagery including visible bands the specific coastline extraction task may be recognized as not particularly complex, this does not hold for SAR images in which the backscattering from the water can be influenced by different effects due to the wind and the wave modulation, determining a not easy discrimination between sea and land. In this research activity a new automatic technique based on Pulse Coupled Neural Networks (PCNN) has been developed to detect the coastal boundaries, moreover a local tracing procedure exploiting statistical information has been designed to properly extract the coastline. The results have been validated through a GPS survey and an assessment of the real impact of the proposed procedure in coastal mapping application has been carried out.

Automatic object extraction from VHR satellite SAR images using pulse coupled neural networks

In this paper we investigate an unsupervised neural network approach for automatically extracting objects of interest from very high resolution (VHR) SAR images. The technique is based on the use of Pulse-Coupled Neural Networks (PCNN) which is a relatively novel technique based on models of the visual cortex of small mammals. The study discusses the use of PCNN technique in different applications. In a first case the extraction procedure is focused on the detection of buildings. In the second case the segmentation of a dark spot representing an oil spill in a SAR image is considered. The performance yielded by the PCNN is evaluated and critically discussed for a set of new generation of X-band SAR images taken by COSMO-Skymed and TerraSAR-X systems.

Fully automatic oil spill detection from COSMO-SkyMed imagery using a neural network approach

The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.

Automatic procedure for coastline extraction exploiting full-pol SAR imagery and AANN-PCNN processing chain

Coastal environment is worldwide recognized as an important asset for mankind. Relevant threats, such as erosion and changes in the territory caused by anthropogenic activities, should be addressed appropriately to support authorities and environmental organizations. Coastline extraction procedure is a fundamental task in relation to the monitoring of coastal surroundings, public security and study on potential climate change effects. In this work a new method is proposed, which aims at improving the coastline extraction procedure by harnessing Full-Pol SAR imagery and a processing chain constituted by cascading an Autoassociative Neural Network (AANN) and a Pulse-Coupled Neural Network (PCNN). The AANNs, also known as autoencoders, have been widely used in the literature for nonlinear features extraction and component analysis. This kind of neural network is designed to replicate the input into the output layer. When this task is considered as fulfilled, a good compressed input representation must be present in the bottleneck layer, enabling the extraction of significant features. Conversely the PCNNs don’t need training stages, and are proven effective in the image processing and segmentation tasks. Describing the proposed method in a nutshell, during the first stage the AANN aims at extracting features that would help the land-sea separation process; in the next stage, the PCNN aims at producing the final segmentation and helps to perform the coastline extraction task subsequently executed. Major features of the method mainly consist of the complete processing automation and the novel architecture design which chains different neural networks to accomplish the coastline extraction task.