A Human-Centered Framework for the Understanding of Synthetic Aperture Radar Images

The limited usage of SAR data in the end-user community and in applicative contexts testified the failure of the recent literature, in which the research privileged the automatic extraction of information at the expense of users' experience with data. The development of new products and processing frameworks providing user-friendly representations and extraction of the physical information is a necessary condition for the full exploitation of SAR sensors. In this Book, the necessity to restore users’ centrality in remote sensing data analysis is analyzed and achieved through the introduction of two new classes of RGB SAR products obtained via multitemporal processing, whose principal characteristics are the ease of interpretation and the possibility to be processed with simple, end-user-oriented technique. These proposed approach aims to definitely fill the gap between the academy and the applications. The rationale is to provide ready-to-use images, in which the technical expertise with electromagnetic models, SAR imaging and image processing has been absorbed in the products formation phase. In such way, the idea that SAR images are too complicated to be interpreted and processed without a high technical expertise in order to extract physical information is overcame

SAR Ship Detection for Rough Sea Conditions

In the Synthetic Aperture Radar (SAR) framework many detection algorithms and techniques have been published in the recent literature; however the detection of vessels whose dimensions are in the order of the image spatial resolution is still challenging in rough sea state scenarios. This issue is addressed in the paper presented here by comparing rationale and performance of two detectors developed by the same authors: the Generalized Likelihood Ratio Test (GLRT) and the Intensity Dual-Polarization Ratio Anomaly Detector (iDPolRAD). Both detectors are tested on a dual-polarization VV/VH Interferometric Wide Swath Sentinel-1 image acquired over the Suruga Bay on the Pacific Coast of Japan. The theory is presented here and the two detectors are compared against the Cell Average-Constant False Alarm Algorithm (CA-CFAR) showing both better performance than CFAR in terms of false alarms rejection

river morphology monitoring using multitemporal sar data preliminary results

AbstractIn 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-α 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-α 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

Hydrological modeling in ungauged basins using SAR data

In this paper we propose a methodology devoted to exploit high resolution radars for monitoring water bodies in semi-arid countries. The proposed approach is based on appropriate registration, calibration and processing of SAR data, producing information ready to use by end-users. The obtained results were used to (i) estimate a relationship between surface and volume of water stored in reservoirs and (ii) validate a hydrological model that simulates the time evolution of water availability

Sentinel-1 for Monitoring Reservoirs: A Performance Analysis

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

Near Real-Time Volumetric Estimates Using Unmanned Aerial Platforms Equipped with Depth and Tracking Sensors

Volume estimation of specific objects via close-range remote sensing is a complex task requiring expensive hardware and/or significant computational burden, often discouraging users potentially interested in the technology. This paper presents an innovative system for cost-effective near real-time volume estimation based on a custom platform equipped with depth and tracking cameras. Its performance has been tested in different application-oriented scenarios and compared against measurements and state-of-the-art photogrammetry. The comparison showed that the developed architecture is able to provide estimates fully comparable with the benchmark, resulting in a quick, reliable and cost-effective solution to the problem of volumetric estimates within the functioning range of the exploited sensors

High level semantic land cover classification of multitemporal SAR images using synergic pixel-based and object-based methods

Land cover mapping is one of the classic applications of synthetic aperture radar remote sensing. However, despite of the algorithmic progress in classification techniques, the semantic content of available maps does remain unchanged, with only a few macro-classes (like water, forest, urban, and bare soil) being discriminated in the majority of the works from past years. In this paper, a methodology to extract a higher level semantics from synthetic aperture radar images is presented. It is based on coupling pixel-based clustering with object-based image analysis and contextual information. Preliminary results have been produced from multitemporal SAR datasets over a forest area in Colombia. They demonstrate that the synergic exploitation of pixel and object information can provide higher quality land cover results and more information to map users

Multitemporal SAR RGB Processing for Sentinel-1 GRD Products: Methodology and Applications

The Sentinel-1 mission has finally reached its maturity with the launch of the second Sentinel radar. Among the products delivered by the agency, the ground range detected class is raising more and more interest among users due to its reduced computational demand for information extraction and availability on cloud exploitation platforms, like the Google Earth Engine. In this paper, we present a novel multitemporal processing chain, suitable to be applied to Sentinel-1 ground range detected products to obtain RGB images, using a series of single polarization detected images. These products aim at being the equivalent for the recently introduced Level-1 $\alpha$ , exploiting a texture measure instead of the interferometric coherence, to properly render and enhance the presence of built-up areas. The discussion is supported by experiments showing the reliability of this newly introduced class of products in classic synthetic aperture radar applications like image photointerpretation, flood mapping, and long-term urban area monitoring