Search

There has been a significant increase in wildfires in recent years due to climate change, which is having an increasing impact on human settlements, infrastructure, buildings and the environment [1]. The statistics published by the California Department of Forestry and Fire Protection states that the area affected by fires in 2021 (2.6 million acres) is approximately 47% higher than the average over the last five years of 1.6 million acres [2]. Mitigating and preventing wildland fires is a crucial task that involves strategic planning and detailed monitoring of fire activity. Many remote sensing techniques have been established to aid emergency responders with the immediate planning and actions to be taken in the event of wildfire. Synthetic Aperture Radar’s (SAR) capability of penetrating the clouds and smoke offers a unique advantage for monitoring the progression of wildfire, which can be quite challenging to contain [3]. The capability of coherent change detection applied to interferometric SAR images for describing changes associated with wildfire is presented here. The coherence from interferometric SAR has been shown in many case studies to be an effective tool for monitoring deforestation and forest fires [4]. An increase in time interval between images leads to increased variation in the scatterer’s distribution which results in lower coherence values, which can reduce the usefulness of this method. The value of coherence is much more evident in forest areas when a shorter interval between subsequent images can be utilized. . The Rum Creek fire burned 21,347 acres in southwest Oregon, USA, which was ignited by lightning strikes on August 17, 2022 and continued for nearly a month. Two ICEYE stripmap SAR images (August 26, 2022 and August 28, 2022) captured the scene during the fire event and demonstrated the effectiveness of using interferometric SAR methods in detecting the burn scars. The results show good agreement with the fire perimeter released by the Wildland Fire Interagency Geospatial Services (WFIGS) Group [5]. References: [1] A. Borunda, “The science connecting wildfires to climate change”, https://www.nationalgeographic.com/science/article/climate-change-increases-risk-fires-western-us [2] California Department of Forestry and Fire Protection [online] https://www.fire.ca.gov/stats-events/ [3] A. Moreira, P. Prats-Iraola, M. Younis, G. Krieger, I. Hajnsek and K. P. Papathanassiou, "A tutorial on synthetic aperture radar," in IEEE Geoscience and Remote Sensing Magazine, vol. 1, no. 1, pp. 6-43, March 2013, doi: 10.1109/MGRS.2013.2248301. [4] S. Takeuchi and S. Yamada, "Monitoring of forest fire damage by using JERS-1 InSAR," IEEE International Geoscience and Remote Sensing Symposium, 2002, pp. 3290-3292 vol.6, doi: 10.1109/IGARSS.2002.1027159. [5] The Wildland Fire Interagency Geospatial Services [online] https://data-nifc.opendata.arcgis.com/datasets/nifc::wfigs-current-wildland-fire-perimeters/about

Cross-track synthetic aperture radar (SAR) interferometry is a powerful technique that analyzes the phase shift each pixel undergoes between acquisitions of the same scene with just a slight change of viewpoint. These phase shifts provide information about the topography and, when more than two acquisitions are available at different dates, about possible slow motions along the line of sight, related to subsidence and/or thermal dilation, of the dominant scatterer in the resolution cell. However, interferometric processing exploits phase-only data, does not provide scatterer distribution in the vertical direction, and is not able to separate multiple scatterers lying in the same range/azimuth resolution cell.

Static structures, such as bridges and dams, are in need of continuous structural health monitoring due to the risk of collapse or destabilization, which leads to casualties and material loss. From economical point of view, the frequent inspection of these structures is cost-ineffective and time consuming. This shortage is overcome by using remote sensing systems such as synthetic aperture radar (SAR) systems, that do not require the in situ positioning of sensors and observe the Earth on a very large spatial scale and at regular time intervals. In particular, Tomographic Synthetic Aperture Radar (TomoSAR) has proved to be an effective tool for structure health monitoring. It is a multi-dimensional imaging technique that exploits stacks of interferometric SAR images acquired with slightly different view angles and at different times. TomoSAR is able to analyze the temporal deformations and the millimeter displacements on buildings. The small movements that occur due to thermal sensitivity of the structure can be also captured, so that the coefficient of thermal expansion, which is a fundamental parameter in health structure monitoring, can be computed. The widespread deterioration and some recent collapses of infrastructures have highlighted the importance of developing early warning monitoring strategies, devoted to identify structural problems before they become critical. A problem to be considered is that effective monitoring techniques have to rely on approaches capable of identifying few representative points, which describe well the dynamical behavior of the structure. A TomoSAR technique recently developed, based on a multi-step binary hypothesis GLRT test, has shown a good capability of selecting reliable control points on the structure for estimating its deformations. In order to demonstrate the potentialities of this technique for structure health monitoring, two tall structures will be considered. The results obtained using different SAR sensors, such as COSMO-SkyMed, Sentinel-1A and TerraSAR-X, are presented.

This paper is focused on the problem of the detection of multiple scatterers in synthetic aperture radar (SAR) tomography. The method presented exploits the a priori information that at most $K_{{\rm{max}}}$ different scatterers are present in the same range-azimuth resolution cell. In particular, a simplified version of a generalized-likelihood ratio test (GLRT) detector, based on support estimation (Sup-GLRT), is proposed. The Sup-GLRT is a constant false alarm rate sequential test that detects the presence of scatterers, one after another, and estimates their positions, detecting the support of the unknown signal. The proposed simplified test denoted as Fast-Sup-GLRT detector, despite still being a multistep statistical hypothesis test, exploits, at each step i , an approximated maximum-likelihood estimate of the signal support of cardinality i −1, based on the sequential estimation of i −1 supports of cardinality one. The introduced approximation allows a considerable reduction of the computational complexity, which from the combinatorial trend of Sup-GLRT passes to the linear one of Fast-Sup-GLRT, without significantly impairing the detection probability. The performance of the proposed approach is analyzed using TerraSAR-X system parameters, with particular reference to the elevation superresolution achievable for an assigned probability of false alarm and with a given number of acquisitions. Numerical results on simulated and real data are presented and discussed.

In this paper, the potential of a deep learning approach for SAR tomography (TomoSAR) is investigated. TomoSAR is a powerful technique that allows the 3D reconstruction of objects lying on the Earth surface, by separating multiple scatterers with different elevations laying in the same range-azimuth resolution cell. In urban applications, the number of interfering scatterers is typically very small, so that the reconstruction of the elevation reflectivity profile can be faced as a statistical detection problem. Detection performance depends on how well the adopted statistical model fits to the observed scene. For complex urban scenarios this issue can greatly impair achievable accuracy of results. Then, we propose to exploit the neural networks’ capabilities to learn the data generative model, in order to face the problem of signal model inaccuracies. In particular, in the assumption of a single scatterer, a neural network can be trained to solve a simple classification problem. Results on simulated and real data are presented.

SAR Tomography (TomoSAR) is a multidimensional imaging technique that has proven its ability in localizing multiple scatterers in the three dimensional observed scene, allowing the reconstruction of the elevation profile of the structures on the ground. Tomographic approaches usually estimate the elevation distribution of the scetterers in each range-azimuth pixel independently from the neighboring ones (local approaches). Then, any relation among the elevations of neighboring pixels is imposed in the tomographic processing. In this paper a local contextual information contained in the data is exploited with the aim of improving the 3D reconstruction (semi-local approaches) and increase the number of reliable reconstructed scatterers in the tomographic scatterers cloud. Results on real data validate the proposed approach.

Earth monitoring from space has been made possible with the increase in number of Synthetic Aperture Radar (SAR) sensors and the availability of data of the same scene with frequent revisiting time. The flood mapping using SAR sensors provide immediate response map. In this paper, the flood mapping with Sentinel-1 data using Mahalanobis classifier on preprocessed SAR data and texture features exploited from co-occurrence pixels to distinguish flooded and non-flooded area is introduced. The accuracy of the introduced technique is estimated using the available ground truth. The 2015 South India floods that devastated the metropolitan city of Chennai is revisited using the SAR acquisitions and the corresponding flood map using the introduced technique is derived and the results are analyzed.

The maps and images of geographical information system (GIS) are used for finding locations, accessing rail, bus routes, and for educational purposes such as study on vegetation, landscapes, population, and so on and so forth. Remote sensing is the process of acquiring data about Earth by using satellites or satellite-borne or airborne sensors. The images acquired through remote sensing systems are integrated within GIS to store, analyze, and manipulate geographical information of the Earth. The huge size of the digital raster maps makes compression inevitable in particular to reduce the transmission time and display them on the Internet as well as other networks. In this paper, a lossless coding approach that performs encoding on the decomposed binary layers by taking the advantage of binary wavelet transform that produces sparse matrix for row column reduction and Huffman coding is presented. The results obtained on raster maps are compared with those of other existing techniques.

Earth observation using spaceborne sensors with short revisiting time has forgone the limits of conventional assessment methods. In this paper, we investigate the use of SAR Tomography (TomoSAR) to monitor infrastructures, using Morandi Bridge in Genoa, Italy, as a case study. Morandi Bridge suffered a partial collapse due to a structural failure on August 14, 2018. The main issues to be considered for using TomoSAR as a means of an early warning system in structural health monitoring are discussed, highlighting the differences respect to monitoring applications dealing with subsidence and deformation of extended areas.A set of 70 Sentinel-1A images of the bridge over a period of three years prior to the bridge collapse are analyzed using 5D TomoSAR based on Sup-GLRT scatterers detection technique. Preliminary results indicate that possible temporal and thermal deformations of the static structure can be estimated using the method considered.

This article aims on describing a solution for optimizing the computational cost of a tomographic Synthetic Aperture Radar (TomoSAR) technique, using a Fast Support Generalized Likelihood Ratio Test (Fast-Sup-GLRT), for monitoring large-scale areas. The solution presented uses MapReduce framework, which has been built on parallel computing model. TomoSAR techniques are capable of estimating intensity, altitude, temporal deformation and thermal dilation of scatterers laying on the earth surface by means of multiple acquisitions of satellite-borne or airborne SAR sensors. Then, they can provide a dynamic profile of urban environment. A problem to be faced is the very high computational cost involved in the monitoring the wide areas using a large number of SAR images. The computational burden for exploring larger areas can be handled using the parallel paradigm. The experimental performance analysis on real datasets with the quantitative analysis of the computational efficiency are presented in this paper.

In this paper, persistent scatterer interferometry and Synthetic Aperture Radar (SAR) tomography have been applied to Sentinel-1 data for urban monitoring. The paper analyses the applicability of SAR tomography to Sentinel-1 data, which is not granted, due to the reduced range and azimuth resolutions and the low resolution in elevation. In a first part of the paper, two implementations of the two techniques are described. In the experimental part, the two techniques are used in parallel to process the same Sentinel-1 data over two test areas. An intercomparison of the results from persistent scatterer interferometry and SAR tomography is carried out, comparing the main parameters estimated by the two techniques. Finally, the paper addresses the complementarity of the two techniques, and in particular it assesses the increase of measurement density that can be achieved by adding the double scatterers from SAR tomography to the persistent scatterer interferometry measurements.

This paper addresses the problem of SAR Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers in presence of partially correlated Gaussian clutter. TomoSAR is a multidimensional imaging technique that has proven its ability in localizing the scatterers, reconstructing the elevation profile of the structures on the ground (3D reconstruction) and estimating the temporal deformations and thermal dilations of the scene (5D reconstruction). In the literature statistical based TomoSAR reconstruction refers to a signal model where in each range-azimuth resolution cell one or more scatterers are interfering in presence of noise and clutter signals, modeled as zero-mean complex circular white Gaussian random vectors. In this paper, we propose to extend a generalized likelihood ratio test (GLRT) detector, proposed by the authors and denoted Fast-Sup-GLRT, to a different signal model, where a correlated clutter model is considered. Results on TerraSAR-X real data are presented.

In the last decade, Persistent Scatterer Interferometry (PSI) and SAR tomography (TomoSAR) have been used for reconstructing the elevation profile of a scene, starting from a set of co-registered Synthetic Aperture Radar (SAR) images. The possible advantage of TomoSAR over classical interferometric methods consists in the potential capability of improving the detection of single scatterers presenting stable proprieties over time (Persistent Scatterers or PS), as well as to enable the detection of multiple scatterers interfering within the same range-azimuth resolution cell. In urban environment, when only single dominant scatterers are present in each range-azimuth resolution cell, both methods can be exploited to estimate the altitude, deformation rate and thermal expansion of a subset of reliable scatterers, which are selected on the basis of different criteria. This paper is focused on a performance analysis of the two class of methods, using the results obtained in urban environment on simulated and real TerraSAR-X data. A concise description of both techniques, along with a discussion on their potential capabilities in selecting the most reliable scatterers, is given.

In this manuscript, a technique based on Bayesian estimation theory for filtering Synthetic Aperture Radar images is presented. The technique applies a Wiener filter to the available data, after an homomorphic transformation. With respect to classical a Wiener filter approach, the algorithm has two main advantages: it is able to take into account the spatial correlation among noise samples and it is able to automatically adapt the filter behavior to the image characteristics. This allows an improvement of the filter accuracy, without increasing the algorithm complexity: the good performances in terms of time consuming of Wiener filter are well preserved. Results on real datasets are reported and compared to the ones achievable using other filters existing in literature, showing the effectiveness of the approach.

A novel approach for speckle reduction in SAR images is presented. An enhanced version of the Wiener Filter is proposed in order to locally adapt the filter characteristic to the image behavior, modelled by Markov Random Fields. First results on simulated and real data are reported.

In this manuscript, a technique for speckle noise reduction in ultrasound images is presented. The method exploits Wiener filter and is able to take into account spatial correlation among noise samples. With respect to classical Wiener filter approach developed in independence hypothesis, the methodology is able to sensibly improve filtering performances, at the cost of no computational time increase. Results on realistic simulated datasets are reported, showing the effectiveness of the approach.

In this paper we focus on the detection of single and double scatterers in SAR tomography. In particular, the performance of a support based Generalized Likelihood Ratio Test (GLRT) approach is analyzed, using TerraSAR-X system parameters, with particular reference to the elevation resolution achievable for an assigned probability of false alarm and with a given number of acquisitions. Results on simulated and real data are presented.

In this paper the problem of despeckling Synthetic Aperture Radar images is addressed. An algorithm developed in the Bayesian estimation theory framework is presented. In particular, considering single look images, the algorithm applies an homomorphic filter followed by an Iterative Wiener filter to reduce the speckle. The proposed approach is tested on simulated and real X-band datasets showing interesting noise reduction capabilities.