Donato Amitrano

Abstract

In this paper, we present a new framework for the RGB composition of SAR data. The proposed processing chain aims at providing products particularly oriented toward the end-user community, thus characterized by a high degree of interpretability and by the possibility to be processed with simple algorithms for information extraction. The physical rationale of the proposed RGB products is presented through examples highlighting their principal properties. Finally, their suitability with applications is demonstrated through two activities dealing with urban area and land cover mapping.

Abstract

In this paper, we introduce an innovative method to map small reservoirs in semi-arid environment. The proposed technique is based on the exploitation of the recently introduced Level-1α RGB SAR composite, and it is based on change detection. In particular, we introduce a user-oriented water index defined by a weighted ratio of the bands composing the RGB product. The performance of the method are evaluated by comparison with other popular segmentation techniques of the same complexity in order to demonstrate its reliability. Finally, an applications concerning hydrology, i.e the estimation of reservoir bathymetry starting from area measurements, is presented to underline the usefulness of remote sensing in multidisciplinary contexts.

Abstract

Earth observation technologies can provide a significant contribution to the monitoring urban areas and critical infrastructures. In this paper, we show how to exploit the recently introduced multitemporal SAR RGB images of the Level-1α and Level-1β family in these applications. Simple, ad hoc algorithms are discussed to adapt these generalist products to the specific case study. In particular, self-organizing map clustering and object-based image analysis are used for urban area mapping. As for infrastructure monitoring, an application concerning railway monitoring is discussed.

Abstract

In this paper, we test the capability of satellite synthetic aperture radar (SAR) images to enhance the monitoring of river geomorphological processes. The proposed approach exploits the recently introduced Level-1α products. These products are bi-temporal RGB composites in which the association color-object, being physical-based, is stable whatever the scene is considered. This favors the detection of temporary rivers' characteristics for classification purposes in a change-detection environment. The case study was implemented on the Orco river (northwest Italy), where a set of 39 COSMO-SkyMed SAR stripmap images acquired from October 2008 to November 2014 was used to monitor channel planform changes. This preliminary study is devoted to assess the suitability of Level-1a images for geomorphologist, with particular reference to the detection of phenomena of interest in river monitoring. This is prior for semi-automatic or automatic classification activities.

Abstract

In this paper, we introduce a new framework for the RGB representation of SAR data. The purpose is to lower the expertise required to manage data, providing intermediate products between the classic Level-1 and Level-2. These products are particularly oriented toward the end-user community. In fact, their principal characteristics are interpretability, reproducibility and possibility to be processed with simple algorithms, widely available in the most popular software suites. The proposed framework and products are discussed through examples in which the above-mentioned properties are highlighted. Finally, their suitability in applicative contexts is demonstrated through two example concerning classification activities, implemented exploiting both pixel-based and object-based techniques.

Abstract

In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal synthetic aperture radar (SAR) data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1β products are particularly oriented toward nonexpert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of this paper is the design of a suitable RGB representation of data aiming to enhance the information content of the time-series. The physical rationale of the products is presented through examples, in which we show their robustness with respect to sensor, acquisition mode, and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new European Space Agency sensor. Finally, we propose two applications based on the use of Kohonen’s self-organizing maps dealing with classification problems.

Abstract

In this paper, we present a technique for improving the representation of built-up features in model-based multitemporal synthetic aperture radar (SAR) RGB composites. The proposed technique exploits the multitemporal adaptive processing (MAP3) framework to generate an a priori information which is used to implement an adaptive selection of the coherence window size. Image texture is used to support the coherence information in case of decorrelation. The coherence information, powered by texture analysis, and combined with backscattering amplitude, provides a unique representation of built-up features. This allows for an immediate detection of urban agglomerates by human operators, and is an advantaged starting point for urban area extraction algorithms.

Abstract

In this paper, we present an end-user-oriented framework for multitemporal synthetic aperture radar (SAR) data classification. It accepts as input the recently introduced Level-1α products, whose peculiarities the high degree of interpretability and the increased class separability with respect to single grayscale images. These properties make the Level-1α products very attractive for the application of simple supervised classification algorithms. In fact: i) the high degree of interpretability of the maps makes the training phase extremely simplified; ii) the good separation between the classes allows for obtaining excellent results using simple discrimination rules. It results in a simple, fast, accurate and repeatable framework.

Abstract

The use of remote-sensing images is becoming common practice in the fight against environmental crimes. However, the challenge of exploiting the complementary information provided by radar and optical data, and by more conventional sources encoded in geographic information systems, is still open. In this work, we propose a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment. The data fusion is performed at a feature-based level. Experiments on data available for the area of Caserta, in southern Italy, show that the proposed technique provides very high detection capability, up to 95%, with a very low false alarm rate. A fast and easy-to-use system has been realized based on this approach, which is a useful tool in the hand of agencies engaged in the protection of territory.

Abstract

This paper presents the Multitemporal Adaptive Processing (MAP3) framework for the treatment of multitemporal Synthetic Aperture Radar (SAR) images. The framework is organized in three major activities dealing with calibration, adaptability and representation. The processing chain has been designed looking at the simplicity, i.e. the minimization of the operations needed to obtain the products, and at the algorithms availability in literature. Innovation has been provided in the cross-calibration step, which is solved introducing the Variable Amplitude Levels Equalization (VALE) method, through which it is possible to establish a common metrics for the measurement of the amplitude levels exhibited by the images of the series. Representation issues are discussed with an application-based approach, supported by examples regarding semi-arid and temperate regions in which amplitude maps and interferometric coherence are combined in an original way.

Abstract

In this paper we explore the performances and the opportunities provided by the European satellite Sentinel-1 for water resource management applications in low-income countries. The analysis is supported by a synthetic aperture radar (SAR) simulator, which allowed the quantification of the expected characteristics of Sentinel-1 products in three applications: interferometric digital elevation models (DEMs) generation, land cover mapping and estimation of water volumes retained by small reservoirs. The obtained results quantitatively show that Sentinel-1 data characteristics are fully suitable for most of the application already explored in the recent SAR literature.

Abstract

We propose a new approach for remote sensing data exploration, based on a tight human–machine interaction. The analyst uses a number of powerful and user-friendly image classification/segmentation tools to obtain a satisfactory thematic map, based only on visual assessment and expertise. All processing tools are in the framework of the tree-structured MRF model, which allows for a flexible and spatially adaptive description of the data. We test the proposed approach for the exploration of multitemporal COSMO-SkyMed data, that we appropriately registered, calibrated, and filtered, obtaining a performance that is largely superior, in both subjective and objective terms, to that of comparable noninteractive methods.

Abstract

In this paper we propose a methodology devoted to exploit the outstanding characteristics of COSMO-SkyMed for monitoring water bodies in semi-arid countries at a scale never experienced before. The proposed approach, based on appropriate registration, calibration and processing of Synthetic Aperture Radar (SAR) data, allows outperforming the previously available methods for monitoring small reservoirs, mainly carried out with optical data, and severely limited by the presence of cloud coverage, which is a frequent condition in wet season. A tool has been developed for computing the water volumes retained in small reservoirs based on SAR-derived digital elevation model. These data has been used to derive a relation between storage volumes and surface areas which can be used when bathymetric information are unavailable. Due to the lack of direct measures of river’s discharge, the time evolution of water volumes retained at reservoirs has been used to validate a simple rainfall-runoff hydrological model which can provide useful recommendation for the management of small reservoirs. Operational scenarios concerning the improvement of the efficiency of reservoirs management and the estimation of their impact on downstream area point out the applicative outcomes of the proposed method.

Abstract

This paper presents an efficient framework and a sustainable pilot project on the effective use of spaceborne Synthetic Aperture Radar (SAR) in low income countries and semi-arid climatic contexts. The technical efficiency was pursued by integrating SAR models and hydrological assessment methods; the socioeconomical sustainability was guaranteed by the joint work of scientists, technicians and volunteers. The pilot project was developed in the Yatenga region, a Sahelian area in northern Burkina Faso. In particular, an original development of SAR Interferometry algorithms was tailored to the peculiar climate, the soil characteristics, and the land-cover of the semi-arid regions. A digital elevation model (DEM) was derived and an original approach based on the use of SAR amplitude images is proposed for its validation. The achieved DEM resolution (9m) is significantly better than that of the previously available DEMs in the study area (30m). Based on the DEM, the soil sedimentation rate of small reservoirs has been estimated together with the average soil-loss in the contributing catchments due to the erosion process. A multi-temporal filter was implemented on the SAR images for monitoring of water intake volume in small reservoirs, and its seasonal evolution. The developed tools provide an innovative contribution for the improvement of water resource management in the study area. This approach is repeatable and scalable to suit situations with similar economic and climatic conditions.

Details

This package includes a Level-1β product of the Dresden area built using 6 Sentinel-1 images acquired between October and December 2014 and its corresponding 5-class land cover map obtained through self-organizing map clustering of the input RGB product.

Reference
D.Amitrano, F. Cecinati, G. Di Martino, A. Iodice, P.-P. Mathieu, D. Riccio, G. Ruello, "Multitemporal Level-1β Products: Definitions, Interpretation, and Applications", IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 11, pp. 6545-6562, November 2016

Details

This package includes the codes implementing a mode filter for post-classification activity. The filter substitutes the central pixel of a moving window with the mode computed in the window itself. Vectorization and tiling are implemented to optimize computational time and to manage large images.

Reference
D.Amitrano, G. Di Martino, A. Iodice, D. Riccio, G. Ruello, "An end-user-oriented framework for the classification of multitemporal SAR RGB images", International Journal of Remote Sensing, vol. 37, no. 1, pp. 248-261, January 2016