Combining polarimetric and contextual information using autoassociative neural networks

In the last decade there has been a considerable development of spaceborne SAR sensors. All the major space agencies are planning future SAR missions with polarimetric capabilities. However there is still a need to guide electromagnetic and statistics theories that take advantage of this kind of information towards operational applications. The use of contextual information is often required for automatic interpretation and target detection. The implementation of fast and reliable algorithms that exploit both polarimetric and contextual information can be limited by the increased dimensionality of the problem. Principal Component Analysis (PCA) is a data analysis technique that relies on a simple transformation of recorded observation, stored in a vector, to produce statistically independent variables. Non-Linear PCA is commonly seen as a non-linear generalization and extention of standard PCA. If non-linear correlations between variables exist, NLPCA will describe the data with greater accuracy and/or by fewer factors than PCA. In this work a combination of polarimetric and contextual information is performed using an Auto Associative Neural Network. A set of polarimetric input features were chosen together with contextual descriptors in order to produce an information set having lower dimensionality that can be exploited in a classi cation problem

A novel processing chain ingesting multi-band SAR data for fully automatic oil spill detection

With the launch of the Italian constellation of small satellites for the Mediterranean basin observation (COSMO)-SkyMed and the German TerraSAR-X missions, the availability of very high-resolution SAR data to observe the Earth day or night has remarkably increased. Moreover, future SAR missions such as Sentinel-1 are scheduled for launch in the very next years. Suitable and adequate image processing procedures are then necessary to fully exploit the huge amount of data available. As far as oil spill detection is concerned, it is known that one of the most critical issues for the implementation of a fully automatic processing chain is the image segmentation. In fact, the extraction of the dark spots in the image is the first of three necessary steps, the other two being its characterisation by using a set of features and the classification between oil spill and look-alike. Different approaches have been discussed in the literature for dark spot detection in SAR images acquired over the oceans. Aside from the accuracy of the segmentation results, one of the most significant parameters for evaluating the performance in this context is the processing time which is necessary to provide the segmented image. This might be crucial both in emergency cases and when stack of images have to be elaborated in a raw. In this paper we present a new fast, robust and effective automated approach for oil-spill monitoring. 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 detection process into two main steps, WMM enhancement and PCNN segmentation

Polarimetric SAR target decomposition based on nonlinear decorrelation

In remotely sensed Synthetic Aperture Radar (SAR) images, scattering from a target is often the result of a mixture of different scattering mechanisms. Fully polarimetric data offers the possibility to separate and to interpret them. To achieve this task, several target decomposition techniques have been proposed in the literature. In particular, the advantage of classical target decomposition techniques applied to fully polarimetric SAR data is due to the fact that by evaluating the scattering matrix, various scattering mechanisms and target properties can be identified. Aim of this paper is to evaluate a novel approach based on the use of Nonlinear Principal Component Analysis for the target decomposition. In fact, differently from classical target decomposition techniques, the proposed method is based on the decorrelation of the polarimetric SAR data to extract the inherent information content related to the different scattering mechanisms present in the image. An assessment of the effectiveness of the nonlinear principal component analysis method for target decomposition has been carried out by comparing it with the classical decompositions

Nonlinear PCA based Polarimetric Decomposition

The latest years demonstrated the operational level reached by polarimetric data processing techniques. The next generation of spaceborne Synthetic Aperture Radar satellites will implement full- or dual- polarimetric capabilities. In few years a huge amount of data will have to be processed in a fast and reliable way, implementing polarimetric decompositions or accurate classifications. Two neural network approaches for fast and accurate processing of polarimetric data are presented. In the first approach a neural network based processing chain for fast model based polarimetric decomposition is developed, while in the second approach a Non-Linear Principal Component Analisys of polarimetric data has been performed using an Auto-Associative Neural Network. The results show a considerable reduction of computational effort and a substantial data compression with a minimun loss of information

Nonlinear PCA based Polarimetric Decomposition

The latest years demonstrated the operational level reached by polarimetric data processing techniques. The next generation of spaceborne Synthetic Aperture Radar satellites will implement full- or dual- polarimetric capabilities. In few years a huge amount of data will have to be processed in a fast and reliable way, implementing polarimetric decompositions or accurate classifications. Two neural network approaches for fast and accurate processing of polarimetric data are presented. In the first approach a neural network based processing chain for fast model based polarimetric decomposition is developed, while in the second approach a Non-Linear Principal Component Analisys of polarimetric data has been performed using an Auto-Associative Neural Network. The results show a considerable reduction of computational effort and a substantial data compression with a minimun loss of information

Use of COSMO-SkyMed constellation for monitoring the post-fire vegetation regrowth: The Capo Figari case study

The use of COSMO-SkyMed constellation has been tested for post-fire monitoring activities. A typical Mediterranean ecosystem, seriously damaged by a wildfire, has been selected as study area. The multitemporal and multipolarization capabilities of COSMO-SkyMed have been exploited for automatic burnt area detection and monitoring of vegetation regrowth. Different imaging configurations have been tested to define the proper monitoring strategy in Mediterranean areas. Results showed X-band suitability for Mediterranean maquis post-fire monitoring