Observation of local Wetland areas from Satellite Imaging (OWETIS) is an international project funded by the European Space Agency (Hungary Industry Incenctive Scheme Project 2016/11-2018/10)
OWETIS is based on the DUSIREF project. In this project we address a new and very important issue: the observation of small backcountry wetland areas surrounded by different areas, hosting important species and delivering essential ecosystem services and biodiversity. Although these patches are small one by one, but together they can contribute to the wetland cover area with a very high rate – their protection and mapping is a need. These small ecosystems act a main role in the Hungarian biodiversity – these many small patches may give an important contribution to the ecosystem in the Carpathian basin.
Partners:
Coming soon.
Multichannel passive ISAR imaging for military applications (MAPIS) is an international project funded by the European Defence Agency (Ad-Hoc Research and Technology Category B Project 2014/12-2017/12)
MAPIS is based on the APIS project, with extended goals: To study, define, analyse a new system concept for implementing and demonstrating ISAR imaging capability in a plug-in multistatic array passive radar finalized to target recognition. The objective of the MAPIS project is to study, define, analyze a new system concept for implementing and demonstrating ISAR imaging capability in a plug-in multistatic array passive radar for target recognition. The activity is focused on the system back-end to allow the passive radar having the following main characteristics:
Partners:
Coming soon.
DUSIREF (Dynamic Urban Scene Interpretation and REconstruction through remotely sensed data Fusion) is a joint project of the Machine Perception Research Laboratory (MPLab) of MTA SZTAKI and Airbus DS Geo Hungary Ltd., funded by the European Space Agency (ESA) under the PECS-HU framework (2013-2016). The main objective of the project is high level urban scene recognition and change interpretation based on heterogeneous Remote Sensing (RS) data sources (mainly optical and TerraSAR satellite images and LIDAR data). We aim to develop novel recognition and visualization methodologies relying on 4 dimensional (3 spatial and 1 temporal) data representation. We focus on highly multi-modal, multi-scaled and multi-temporal data collections, and build a unified database which is appropriate for answering user queries about events or changes in the RS data.
As of June 2015, the project has been extended to observe small natural areas within urban scenes. These natural patches may host important species and deliver essential ecosystem services and biodiversity surrounded by foreign environment.
Virtual city reconstruction, Change Detection in Wetlands
OTKA #101598 "Comprehensive Remote Sensing Data Analysis, was a postdoctoral project funded by the Hungarian Scientific Research Fund between Jan. 2012 and Dec. 2014 (36 months).
Outline: Earth observation is a growing field of interest in various application areas, such as monitoring agricultural activity, detection of pollution and environmental crimes, management of urban area expansion, crisis management, including civil protection, or homeland security. However, the evaluation of the collected remotely needs exhausting human intervention up to now due to the rich and continuously augmenting content and various aspects of assessment. For this reason, necessity of automated recognition problems in remote sensing is raised by both national and international demands.
The work focuses on the research towards a generalized framework and procedure library for representing different targets, hierarchic structures and various levels of changes using remotely sensed 2-D images and 3-D (LIDAR, ISAR or DEM) data. The developed methods attempt to collect similar tasks appearing in different application areas, and handle them in a joint methodological approach. An important feature of the proposed models will be the separation of the data and application dependent elements from the abstract hierarchical structure which has various levels, such as pixel, region, object, object group and land cover class. Task definitions will origin from specific applications, followed by problem grouping, abstraction and generalization.
Featured results: Multi-level Object Population Analysis with an Embedded Marked Point Process model, with applications of automatic traffic monitoring and built-in area analysis. Change detection survey.
APIS (Array Passive ISAR adaptive processing), was a European Defence Agency (EDA) project, "Innovative concepts & emerging technologies" - C3/1 Radar technologies- Processing, 2010-2012
Outline: detection and classification of small slow moving surface targets in presence of sophisticated jamming from airborne platforms is currently one of the more stringent need for new European Defence systems. Advanced radar systems with a high level of flexibility, reconfigurability modularity need high performance adaptive signal processing to fully exploit the radar potentiality.
A possible solution to match these requirements is to make use of passive radar in antenna array configuration. This new system concept is of great interest for operative military scenarios because
1) the absence of electromagnetic emission provides a Low Probability of Intercept (LPI)
2) the system is intrinsically coherent because it directly uses the signal transmitted by the illuminator of opportunity as the reference signal
3) Friendly radar emissions, typically present in the area under surveillance, can be exploited as illuminators of opportunity. Radar waveforms are more efficient than communication transmitted signals (better ambiguity function and spatial resolution).
Moreover, the use of a receiving array antenna allows:
1) Forming multiple beams for separating source reference channel from the surveillance
channel
2) Reducing the direct signal interference, by creating suitable nulls on the beam pattern in the
Partners:
Target Detection, Recognition and Tracking in Inverse Synthetic Aperture Radar (ISAR) Images