Mapping 3D surface deformation using the advanced radar interferometry methods

Mehdi Darvishi

Permanent Scatterers, Small Baseline Subset, Multi-Aperture Interferometry, UAV, TLS

Satellite remote sensing data have already proven their applicability to derive parameters relevant to natural hazards. In particular, differential interferometry (DInSAR) has demonstrated to be suitable for directly measuring geometric surface changes. Using the Corvara slow moving landslide as a test site, the objective of my study is a) to estimate landslide movement/velocity by the time series processing of SAR data, b) to estimate landslide movement/velocity by TLS and UAV, and c) integrate the SAR data with the TLS/UAV and GPS data.

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Multi-Temporal Analysis and Change Detection Techniques in Time Series of Remote Sensing Images

Manuel Bertoluzza

Remote Sensing, Multispectral, SAR, Multi-temporal, Change Detection.

Recently, thanks to constellations of satellites orbiting around our planet, a large amount of remote-sensing images of a fixed location on the surface is available. Nowadays, both optical and Synthetic Aperture Radar (SAR) images are largely available and carry a different type of information on the target area. The formulation of traditional problems like the detection of changes occurred between two images can take advantage of the multi-temporal analysis of time series of images. New approaches are therefore needed to fully exploit the larger amount of information available on an area of interest.

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Development of advanced signal and data processing methods for the analysis of data acquired by planetary radar sounders

Leonardo Carrer

Radar Sounders are valuable instruments for subsurface investigation in both terrestrial and space applications. They are widely employed for monitoring changes to the polar ice sheets and for the study of planetary bodies (e.g. Mars). My research activities are related to radar sounders system engineering, performance models and advanced signal processing.

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Advanced Regression and Detection Methods for Remote Sensing Data Analysis

Davide Castelletti

Remote sensing, radar sounder, sar, semisupervised regression

Radar systems constitute a valuable instrument in the remote sensing. Radar technologies are suitable to address a multitude of applications. My research interests include radar remote sensing data analysis by exploiting novel regression and detection techniques. Moreover, my activity includes the development of performance models for the design of radar sounder instruments for planetary exploration.

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Remote sensing e model data integration for glaciers monitoring

Ludovica De Gregorio

glacier, remote sensing, data integration, machine learning

Cryosphere monitoring plays a key role for an improved understanding of global climate, for Earth system modelling and for the study of hydrologic systems and sea-level change. Different methods, involving surface and remote-sensing measurements and several instruments and sensors, have been developed in order to understand the complex nature of cryosphere dynamics. In this context, the aim of my Phd project is to develop a multi-level data fusion approach, which allows us to merge different data sets (e.g. model outputs and remotely sensed products). The outcome will be a 15 years’ time series of consistent data on mass balance, snow water equivalent and run-off for all major catchments above about 1000 m a.s.l with a nival and glacial run-off regime within the EUREGIO region.

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Stratton-Chu formula-based multilayer radargram simulator

A polyvalent tool for observation strategy validation and data analysis

Christopher Gerekos

radar, radar sounder, simulator, multilayer, facet method, stratton-chu

Radar sounders are valuable instruments for subsurface investigation in both terrestrial and space applications. However, the design of a radar is strongly dependant on the type of terrain it has to investigate. I am working on a radar echo simulator, which would take any geoelectrical model (i.e. any kind of terrain), with an arbitrary number of subsurface layers, and would then simulate how would a particular radar see it, according to its own specificities. Because of its flexibility, this simulator could be useful both as an observation strategy validator for future missions (e.g. JUICE) and as a data analysis tool for ongoing/completed ones.

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Advanced Methods for Change Detection in Multitemporal LiDAR Data and Hyperspectral Images

Daniele Marinelli

Remote Sensing, LiDAR, Hyperspectral Images, Change Detection

In the last years, there have been significant advancements in the remote sensing technology that resulted in new airborne and satellite systems that can acquire data with enhanced resolution. This is the case of high density LiDAR point clouds that accurately characterize the 3-D structure of the analyzed scene and of hyperspectral (HS) images that accurately represent the spectral characteristics of the land-covers. These kinds of data require the development of a new generation of automatic techniques for information extraction. In this context, the objective of this research is the development of advanced methods that exploit the characteristics of LiDAR data and HS images to solve complex change detection problems. We aim to use LiDAR point clouds to identify 3-D changes (e.g. the growth of trees in forest) and HS images to characterize changes associated with specific spectral variations in a given scene (e.g., changes of the status of crops).

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Content based Remote Sensign Image Retrieval in the compressed domain

Akshara Preethy Byju

Content Based Image Retrieval (CBIR) is a technique for retrieving images from the database given a query image based on the similarity in their content. Due to the availability of large number of images and earth observation missions all the images are stored in compressed format for better transmission and storage. Hence, retrieving images from these compressed domain and extracting features from it is one of the most challenging research areas in the field of remote sensing.

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Change detection from multi-temporal multi-sensor VHR images using deep learning

Sudipan Saha

Change detection, Very high resolution, Multi temporal, Multi sensor, Deep learning

The detection of different kinds of changes in multi-temporal images is critical in remote sensing for analysis of landscape variations. Most of the existing methods are suitable for detecting changes from bi-temporal images obtained from same sensor. In addition, current categorization of landscape classes is very coarse, due to poor spatial resolution of satellite images. However, recent technological evolution has resulted in launch of many satellites with higher spatial resolution. Thus, availability of such higher spatial resolution data can be exploited to extract semantically meaningful detailed change information. Existing change detection methods are not designed to use such high resolution images to its full capacity. Current availability of many satellites can be utilized to obtain images at high temporal resolution. However, images from different satellites have different sensor characteristics. In our work, we aim to exploit deep learning due to its suitability for invariant feature representation and semantically rich information extraction. We aim to propose a deep learning based unsupervised framework to extract multiple change information from multi-sensor multi-temporal very high resolution images.

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Advanced Methods for the Analysis of Very High Resolution Multi-Sensor Time-Series Images

Yady Tatiana Solano Correa

Remote Sensing, Image Processing, Very High Resolution images, Change Detection, Multi-sensor Integration, Time Series Analysis

The use of remote sensing in the analysis and evaluation of environmental degeneration processes has become a valuable tool which relevance increased in conjunction with the use of digital image processing techniques. The improvement in acquisition sensor technology as well as in the data processing algorithm allowed an accurate and automatic identification and extraction of characteristics for the understanding of the environmental changes, especially while working with Very High spatial Resolution (VHR) information, acquired by both passive (e.g., IKONOS, QuickBird, GeoEye,WorldView-2, Pleiades) and active sensors (e.g., TerraSAR-X, CosmoSkyMED).Nevertheless, the revisit period of the sensors, the competing orders, and the weather conditions do not always allow the acquisition of proper and relevant information. To mitigate these limitations it is possible to buildtime series by considering images acquired by different sensors. The challenge is how to deal with this kind of information. Therefore, the aims of this proposal are mainly: i) change detection for the analysis in multi-sensor multitemporal VHR images; and ii) multi-sensor multitemporal VHR data homogenization.

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Design and data analysis strategies for Radar for Icy Moons Explorer

Sanchari Thakur

The research involves evaluation of the performance of Radar for Icy Moons Explorer (RIME), an ice penetrating radar onboard Jupiter Icy Moons Explorer (JUICE) mission. Radar sounder data analysis for geological analogues of different RIME targets and development of subsurface geo-electrical models would enable evaluation of RIME to-be-acquired radargrams.

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Advanced methods for the analysis of multispectral and multitemporal images

Massimo Zanetti

Remote sensing, mathematical models

The increasing availability of new generation remote sensing satellite multispectral images provides an unprecedented source of information for Earth observation and monitoring. Multispectral images can be now collected at high resolution covering (almost) all land surfaces with extremely short revisit time (up to a few days), making it possible the mapping of global changes. Extracting useful information from such huge amount of data requires a systematic use of automatic techiques in almost all applicative contexts. In some cases, the strict application requirements force the pratictioner to develop strongly data-driven approaches in the development of the processing chain. As a consequence, the exact relationship between the theoretical models adopted and the physical meaning of the solutions is sometimes hidden in the data analysis techniques, or not clear at all. Altough this is not a limitation for the success of the application itself, it makes however difficult to transfer the knowledge learned from one speci c problem to another. In this project we mainly focus on this aspect and we propose a general mathematical framework for the representation and analysis of multispectral images. The proposed models are then used in the applicative context of change detection. Here, the generality of the proposed models allows us to both: (1) provide a mathematical explanation of already existing methodologies for change detection, and (2) extend them to more general cases for addressing problems of increasing complexity.

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Classification Methods for UAV Imagery

Abdallah Zeggada

Unmanned aerial vehicle extremely high resolution, image multilabeling, multi-object detection, near real-time application, image analysis, coarse description

Unmanned aerial vehicles (UAVs), generally known as drones, have been finding their way into a variety of applications over the last decade. UAVs offer a wide range of advantages, such as for instance the fact of being customizable, small-sized, and easy to use. Another pivotal characteristic underlining UAVs is the possibility to capture images of extremely high resolution, which paves the way to detect details of objects that have not been fully affordable by means of satellite images. In this regard, this research proposal addresses the issue of multiple object detection from UAV-grabbed images, which is particularly regarded as a difficult task taking into account their extremely high spatial resolution. Indeed, traditional recognition schemes are likely to fail in the UAV context due to the extremely high level of details (compared to traditional satellite or airborne based images). Moreover, in some application scenarios, processing times are expected to be very tight, which makes the recognition task even harder. In this respect, we intend in this proposal to put forth classification methods meant to tackle efficiently the complexity of UAV images. Application domains will include urban areas monitoring, precision farming and avalanche rescue.

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