GNSS Remote Sensing: Theory, Methods and Applications

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Signal Processing. Remote Sensing Theory. This tutorial is based on the lectures of Prof. The tutorial is fully interdisciplinary and well suited for participants interested in learning different aspects of the entire end-to-end system of spaceborne SAR systems.

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The deluge of Erath Observation EO data counting hundreds of Terabytes per day needs to be converted into meaningful information, largely impacting the socio-economic-environmental triangle. Multispectral and microwave EO sensor are unceasingly streaming millions of samples per second, which must be interpreted to mine physical parameters to understand Earth patterns and phenomena. Many alternative heterogeneous information sources, as in-situ observations, maps or multimedia information, social networks, are contributing to the overall understanding. The challenge is the exploration of these data for spatio-temporal patterns, relevant information and knowledge, for timely and perpetual global understanding of the phenomena governing the Earth and impacting processes as environmental, socio-economic, etc..


Data Science, specifically for Earth Observation imagery, besides to the prevalent 3Vs, is postulating additional challenges emerging from its very particular nature: data sources are sensors and instruments as multispectral or Synthetic Aperture Radar SAR , information is spatio-temporal, meaning is quantitative as physical parameters and qualitative as semantic descriptors, understanding is contextual in synergy with multi-sensor, in-situ, geoinformation and other sources of information.

Therefore, the goal of the tutorial is the presentation of specific leading edge concepts, methods and algorithms for information content exploration and intelligence extraction from Big Data provided by EO sensors and other related sources. The tutorial offers an cross-disciplinary view of methods in signal processing, machine learning, deep learning, visualization and data mining also addressing the meaning extraction and semantic representations. Looking to the near future technologies, basic elements of quantum information processing will be also presented.

SAR images relative to a same scene and suitable for interferometric processing are today available for most of the Earth, and their number is exponentially growing.


The versatile and available GNSS signals can detect the Earth's surface environments as a new, highly precise, continuous, all-weather and near-real- time. GNSS Remote Sensing: Theory, Methods and Applications (Remote Sensing and Digital Image Processing) [Shuanggen Jin, Estel Cardellach, Feiqin Xie] on.

Archives associated to SAR spaceborne sensors are filled by data collected with time and observation angle diversity multipass-multibaseline data ; moreover, current system trends in the SAR field involve clusters of cooperative formation-flying satellites with capability of multiple simultaneous acquisitions bistatic and multistatic tandem SAR systems , airborne systems with multibaseline acquisition capability in a single pass are also available, and unmanned air vehicles with capability of differential monitoring of rapid phenomena are being experimented.

Focus of this tutorial is on processing methods that, by coherently combining multiple SAR images at the complex phase and amplitude data level, differently from phase-only Interferometry, allow improved or extended imaging and differential monitoring capabilities, in terms of accuracy and unambiguous interpretation of the measurements. The tutorial will cover interrelated techniques that have been shaping in the recent years an emerging branch of SAR interferometric remote sensing, that can be termed Tomographic SAR Imaging, which is playing an important role in the development of next generation of SAR products and will enhance the application spectrum of SAR systems in Earth observation.

Patches are small image parts that capture both texture and local structure information. Though being crude low-level features compared to higher level descriptors, they have led to very powerful approaches in a wide range of image processing tasks such as deblurring, inpainting or classification. They form a core component of most current image restoration techniques. In the past few years, several extensions of patch-based processing have been developed specifically for remote sensing, in particular for hyperspectral and SAR imaging.

Fundamentals of Remote Sensing.

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