In this paper, we propose an innovative concept for an optical payload for Earth Observation, which operates in the medium infrared, based on two emerging technological approaches: super-resolution and compressive sensing. The aim is to improve payload performances in terms of ground spatial resolution and mitigation of some effects, such as saturation and blooming, that are often a limit for obtaining high quality level products in many application domains, such as the detection and monitoring of fire, lava, and, more generally, hotspots. Both approaches are based on the use of a Spatial Light Modulator (SLM), an optoelectronic device consisting of an array of micro-mirrors electronically actuated. The main advantages of the proposed concept consist in: (1) increased ground spatial resolution with respect to the number of pixels of the detector used; (2) expected mitigation of the blooming and saturation effects of the single pixel when high temperature hotspots are observed; (3) compressed-format capture typical of compressive sensing, which eliminates the need for a separate compression card, saving mass, memory and energy consumption.
We use X-band Cosmo-SkyMed InSAR data to highlight several subsidence phenomena resting on some railway and road infrastructures in Lombardia region, Northern Italy, mainly induced by anthropogenic activities. We show eight case studies, namely “Como,” Erba, Oggiono, Valmadrera, Olginate, Verduggio, Melzo, and San Giuliano M., where we detect local subsidence effects affecting several railway and highway lines with deformation rates of about 5 mm/year. The geological features of this part of Italy and the large presence of industrial areas in the surrounding of Milano, Lecco, and Como cities lead to such phenomena. The stability and security of the nested road and railway network could be affected by these surface deformation fields. To guarantee the safety of people, continuous maintenance of the condition of railways and roads together with the monitoring of the conditions of the lands on which they rest on should be done.
This study focuses on testing the SAR coherence changes from Sentinel-1 data to detect burned areas and to compare the results with optical Sentinel-2 derived burned area product to be used as validation. Visible Infrared Imaging Radiometer Suite (VIIRS) data at 350 m resolution was used to identify active fires locations.
We focused on a sequence of wildfires that affected the Sila mountain area during the summer of the 2017. This area of the Calabria region (southern Italy) was interested by a range of fires for the second half of July and the whole month of August ([1], [2]) due also to an extremely dry and hot summer. We used a pair of optical images acquired from Sentinel- 2 satellites on 24 July 2017 (pre-events) and 23 August 2017 (post-events).
Firstly, we computed the Normalized Difference Vegetation Index (NDVI) for both images and calculated the difference between these two (dNDVI) at 10m resolution; the results put in evidence several areas characterized by vegetation reduction, with dNDVI values up to 0.3-0.4. Concerning the SAR data, we evaluated the coherence changes by exploiting two pairs of Sentinel-1 SAR data over the same area. Both pairs were acquired along descending orbit, respectively before (on July, 19th and 31st) and after (on September, 5th and 17th) the fires occurred in the Sila mountain area. The coherence was computed separately for the first (γpre) and the second pair (γpost) and the difference γpost - γpre was calculated. In this way, we evaluated the difference in coherence between September, i.e. post-fires, and July, i.e. pre-fires expecting a higher coherence after burning, due to the vegetation reduction. In several areas, the coherence seems to be consistent with the fire events showing increments up to 0.20-0.25. However, the increasing of coherence difference could also be due to other reasons such as the soil moisture variations in the proximity of lakes/rivers or the seasonal cultivation changes.
Further analysis integrating more information such as the SAR amplitude signal and the cross-polarized backscattering coefficient will be conducted in order to better evaluate and discriminate any contributions.
This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west–south–west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback–Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.
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