In this study, Synthetic Aperture Radar (SAR) methods that exploit both co- and cross-polarized channels are used to detect volcanic deposits caused by the eruption occurred the Sangay Volcano, Ecuador, at the end of November and the beginning of December 2021. First, a change detection scheme, based on the ratio of a pair of images collected before and after the event, is separately applied to the co- and cross-polarized channels. Then, the outputs are analyzed to detect the different phenomena (i.e., lava flow, pyroclastic currents, landslides), that occurred over the scene. Results, undertaken over a pair of images collected before and after the event with the C-band Sentinel-1 Dual Polarimetric (DP) SAR sensor, show that the proposed approach allows for detecting the different phenomena that occurred on the volcanic area. In addition, the co- and cross-polarized channels carried out different information which suggests the combination of them to obtain additional information.

In this study, Synthetic Aperture Radar (SAR) methods are combined with optical ones for earthquake damage assessment after the 2023 Turkey earthquake. First, a change detection scheme, that exploits both SAR and optical images is applied to detect and classify the damaged buildings. Then, the SAR and optical data are combined using a data fusion step based on the k-means algorithm, to improve the reliability and accuracy of the damage assessment map. Results for the city of Kahramanmaras, and undertaken over images collected before and after the event with the X-band COSMO-SkyMed-Second Generation (CSG) SAR sensor and PLANETSCOPE (PLTS) optical sensor rely on demonstrating the effectiveness of the joint use of SAR and optical change detection methods in detecting damage to buildings and infrastructure that can be used for disaster management authorities to prioritize rescue and relief operations in the affected regions.

In this study, a dual-polarization (DP) processing chain is presented to identify lava flow after a volcanic eruption. The proposed methodology is applied to two very challenging case studies, related to the volcanic eruptions that occurred on 24 December 2018 on Mount Etna, Italy, and on 22 May 2021 on Mount Nyiragongo, Democratic Republic of the Congo. The different geomorphology of the two areas together with the different types of lava (i.e., different rheology and rock composition) and surrounding environment is an unprecedented opportunity to analyze the radar polarimetric contribution. First, a DP change detection scheme, which uses a pair of SAR images collected before and after the eruption, is used. Then, the lava flow is detected using a constant false alarm rate detector. Finally, to quantify the performance of the detector, the SAR-based lava flow maps are contrasted with the lava flow footprint provided by Copernicus Emergency Management Service (EMS). Experimental results show that the use of polarimetric information improves lava flow detection in different kinds of environments.

In this study, dual-polarimetric (DP) Synthetic Aperture Radar (SAR) time series collected by the Sentinel-1 SAR mission is exploited to evaluate the changes that occurred over the surface water area of the Po River, in Italy. First, a DP technique based on an incoherent combination of co–and cross-polarized SAR channels are used to enhance the separability between surface river water and land areas. Then, a binary image where the river and the land area are well separated is generated using a constant false alarm rate (CFAR) detector. Once the water-covered area is estimated, an in-depth analysis based on the Shannon entropy is applied to understand the time variability of the water extension of the Po River. Experimental results show that the dual-polarimetric approach can effectively monitor the water body changes that occurred over the river Po basin in an effective way.

This study is to present the lesson learned during the activities related to the Italian Space Agency (ASI) funded APPLICAVEMARS project which aims at estimating sea surface wind field from L-, C- and X-band Synthetic Aperture Radar (SAR) imagery. The paper focuses on the X-band results and it describes a new approach to estimate ancillary wind direction info from the SAR image itself using neural networks.

In this paper, preliminary results obtained within the framework of the Italian Space Agency-funded APPLICAVEMARS project are presented. The project aims at estimating sea surface wind field from L-, C- and X-band Synthetic Aperture Radar (SAR) imagery. In particular, the key element is the adaptation/improvement of geophysical model functions to transform the microwave reflectivity map into an added-value product, i.e., a wind map characterized by a spatial resolution much finer than the one obtainable from scatterometer measurements. First experimental results related to C-band Sentinel-1 SAR imagery are presented.

In this study, dual-polarization (DP) C-band Sentinel-1 SAR imagery are processed to generate a damage map related to an earthquake damaged area. A DP change detection scheme, which uses a pair of SAR imagery collected before and after the earthquake, is used. Finally, to quantify the levels of damage a decision tree procedure is exploited. The damaged area refers to the city of Amatrice and its surroundings that were devastated by the earthquake that hit central Italy on 24 August 2016. To generate the damage map and to verify it, ground information provided by Istituto Nazionale di Ge-ofisica e Vulcanologia (INGV) is processed to generate a new index, termed as Weighted Damage Index (WDI). Numerical results show that an accuracy of about 60% can be obtained that confirms the soundness of the proposed approach.

In this study, a dual-polarization (DP) processing chain is presented to identify burned area in C-band PolSAR imagery and to estimate burn severity. The processing is performed according to a DP change detection scheme that uses a pair of SAR imagery collected before and after the wildfire. Finally, the output is clustered into 3 severity classes. Experimental results obtained processing a Sentinel-1 SAR data set collected over the burned area of Monti Pisani, Italy, confirm the soundness of the proposed approach.

In this study, the capability of polarimetric Synthetic Aperture Radar (SAR) measurements to monitor harsh coastal areas is addressed. A meaningful showcase is presented that refers to the Scottish Solway Firth (SF) wetlands, an important coastal ecosystem severely affected by extreme weather conditions. Experiments are undertaken on a pair of full-polarimetric (FP) C-band RadarSAT-2 SAR images collected over SF during July and November 2018 in order to both extract the coastal profile and to identify changes in the scattering behavior along the coastal strip. The results demonstrate the soundness of the proposed approach and the role played by FP SAR measurements to effectively support vulnerability and risk assessments in harsh coastal environments.

The detection of wind turbines in a strong clutter background is analyzed at variance of polarimetric synthetic-aperture radar (SAR) configurations. The area of interest is the intertidal zone near Jiangsu, China and two detectors are used, the polarimetric notch filter (PNF) and a change detector that optimizes the ratio between covariance matrices. The detection performance is quantitatively analyzed using the receiver operating characteristic (ROC) curve, while the scattering mechanisms that characterize wind turbines are analyzed using the Yamaguchi decomposition. Experimental analysis shows that: 1) wind turbines result in a nontrivial scattering mechanism and 2) full-polarimetric measurements achieve the best detection performance independently of the two detectors.

In this study, multi-polarimetric features extracted from dual-polarimetric (DP) Synthetic Aperture Radar (SAR) data collected by the Sentinel-l SAR mission are exploited to analyze damages due to earthquakes. First, conventional single-polarization features, based on the coherence between pre- and post-event imagery, are analyzed using both the co-polarized and the cross-polarized channels to point out that the information carried on the two channels is different. Then, a coherent dual-polarimetric feature, based on the inter-channel coherence, is proposed. Results show that the latter feature allows to detect the changes after an earthquake using only two SAR acquisitions, i.e.; before and after the earthquake. Finally, a change detection algorithm based on the eigenvalues of the difference of covariance matrices, is proposed. Experimental results show that the DP information allows to improve the results obtained by conventional single polarization change detection techniques.

Automatic Target Recognition (ATR) aims at detecting the presence and at recognizing the typology and the orientation of targets within a scenario, by using an unsupervised approach. In Syntethic Aperture Radar imaging this turns to be a difficult task due to the specific characteristics of clutter and background noise. Within this manuscript a new two-steps ATR algorithm based on Kolmogorov-Smirnov test is presented. The method has been tested on real MSTAR datasets showing interesting performances.