A5010440425- Remote-Sensing Image Classification
- Image Retrieval and Classification Techniques
- Advanced Image and Video Retrieval Techniques
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Geographic Information Systems Studies
- Planetary Science and Exploration
- Atmospheric and Environmental Gas Dynamics
- Historical and Literary Studies
- Spacecraft Design and Technology
- Remote Sensing and Land Use
- Geophysics and Gravity Measurements
- Historical and Literary Analyses
- Atmospheric Ozone and Climate
- French Literature and Criticism
- Satellite Image Processing and Photogrammetry
- Advanced SAR Imaging Techniques
- Geomagnetism and Paleomagnetism Studies
- Geochemistry and Geologic Mapping
- Image and Signal Denoising Methods
- Advanced Computational Techniques and Applications
- Advanced Data Compression Techniques
- GNSS positioning and interference
- Infrared Target Detection Methodologies
- Meteorological Phenomena and Simulations
- Data Management and Algorithms
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
2014-2023
Sorbonne Université
2021
Artificial Intelligence in Medicine (Canada)
2021
Universitatea Națională de Știință și Tehnologie Politehnica București
2021
Asklepios Klinik St. Georg
1969
IBM (United States)
1967
Barmherzige Schwestern Krankenhaus Wien
1905
Data compression techniques are classified into four categories which describe the effect a method has on form of signal transmitted. Each category is defined and examples in each given. Compression methods have received previous investigation, such as geometric aperture methods, well not much attention, Fourier filter, optimum discrete variable sampling rate compression, described. Results computer simulations with real data presented for terms rms peak errors versus ratio. It shown that,...
Currently, the amount of collected Earth Observation (EO) data is increasing considerably with a rate several Terabytes per day. As consequence this volume, new concepts for exploration and information retrieval are urgently needed. To end, we propose to explore satellite image via an mining (IIM) approach in which main steps feature extraction, classification, semantic annotation, interactive query processing. This leads process chain robust taxonomy retrieved categories capitalizing on...
Users of remote sensing images analyzing land cover characteristics are very much interested in classification schemes that define a consistent set target categories. Up to now, number established mainly being used by interpreters medium-resolution optical satellite focusing on large-scale cover. In contrast, we concentrate this publication the definition new scheme for high-resolution synthetic aperture radar (SAR) mostly taken over built-up areas. Here, can see many small details...
Quantum Machine Learning (QML) models promise to have some computational (or quantum) advantage for classifying supervised datasets (e.g., satellite images) over conventional Deep (DL) techniques due their expressive power via local effective dimension. There are, however, two main challenges regardless of the promised quantum advantage: 1) Currently available bits (qubits) are very small in number, while real-world characterized by hundreds high-dimensional elements ( <italic...
The functionality and characteristics of six different data processors (i.e., retrieval codes in their actual software hardware environment) for analysis high‐resolution limb emission infrared spectra recorded by the space‐borne Michelson Interferometer Passive Atmospheric Sounding (MIPAS) have been validated means a blind test experiment based on synthetic spectra. For this purpose self‐consistent set atmospheric state parameters, including pressure, temperature, vibrational temperatures,...
In this paper, we deal with the integration of multiple sources information such as Earth observation (EO) synthetic aperture radar (SAR) images and their metadata, semantic descriptors image content, well other publicly available geospatial data expressed linked open for posing complex queries in order to support analytics. Our approach lays foundations development richer tools applications that focus on EO analytics using ontologies data. We introduce a system architecture where common...
In this paper, we consider the problem of remote sensing image classification, in which feature extraction and coding are critical steps. Various methods aim at an abstract discriminative representation. Most them either theoretically too complex or practically infeasible to compute for large datasets. Motivated by observation, propose a simple yet efficient method within bag-of-words (BoW) framework. It has two main innovations. First most interestingly, does not need any local extraction;...
The increased availability of high-resolution synthetic aperture radar (SAR) satellite images has led to new civil applications these data. Among them is the systematic classification land cover types based on patterns settlements or agriculture recorded by SAR imagers, in particular identification and quantification temporal changes. A (re)classification shall allow assignment continuously updated semantic content labels local image patches. This necessitates a careful selection...
With more and SAR applications, the demand for enhanced high-quality images has increased considerably. However, entail high costs, due to limitations of current devices their image processing resources. To improve quality reduce costs generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) generate images. This method is based on analysis hierarchical information “dialectical” structure GAN frameworks. As demonstration, typical example will be shown, where...
Synthetic aperture radar (SAR) image change detection is playing an important role in various Earth Observation (EO) applications. There exists a large number of different methods that have been proposed to address this issue. However, due the fact several kinds changes with diverse characteristics can arise SAR images, there no consensus on their performances because most evaluated using data sets, probably facing changes, but without in-depth analysis changes. Therefore, two problems...
In this letter, we carry out a comparative study of statistical models for multilook synthetic aperture radar amplitude images. Ten state-of-the-art are selected comparison. To achieve fair evaluation, estimate all model parameters using the method log-cumulants and apply to an image pyramid with varying pixel spacing (and resolution). The is created by different product generation options. addition resolution, also consider homogeneity scene performance evaluation three measures. Through...
We propose a multilevel semantics discovery approach for bridging the semantic gap when mining high-resolution polarimetric synthetic aperture radar (PolSAR) remote sensing images. First, an Entropy/Anisotropy/Alpha-Wishart classifier is employed to discover low-level as classes representing physical scattering properties of targets (e.g., low-entropy/surface scattering/high anisotropy). Then, images are tiled into patches and each patch modeled bag-of-words, histogram class labels. Next,...
In this letter, a velocity estimation method for moving ships in synthetic aperture radar (SAR) images is proposed based on subaperture decomposition technique. contrast to traditional methods, our needs only few SAR imaging parameters besides the image itself. The behavior of theoretically analyzed, and ship motion azimuth direction are accurately estimated. approach was tested real stripmap acquired by TanDEM-X, twin satellite constellation. estimated velocities perfectly fit data recorded...
When we perform image content classification by appending semantic labels to regularly cut patches, have be sure that the selected patch size is a good choice for images at hand. In following, look SAR (Synthetic Aperture Radar) satellite images, and analyse impact of on attainable accuracy. For test with precisely known ground truth, one can determine true precision / recall performance applied method. our case, interactively trained classifier system via active learning, compared resulting...
In this article, we propose a promising approach for the application-oriented content classification of spaceborne radar imagery that presents an interesting alternative to popular current machine learning algorithms. following, consider problem unsupervised feature-free satellite image with already known classes as explainable data mining regions no prior information. Three important issues are addressed here: explainability, feature independence, and unsupervision. There is increasing...
Intelligent query and retrieval techniques from remote sensing archives become more important with the increasing number of satellites in orbit acquiring data. At same time resolution sensors produces images higher complexity. To allow easy access to this information, thus enhance usage data, content-based are mandatory. The authors present structure a new intelligent image archive providing by content. First they capture information using family robust signal models which not selected...
Abstract. A climatological validation of the 6-hourly operational ECMWF troposphere and lower stratosphere temperatures as well geopotential heights between 1000 10 hPa is performed using 2001–2007 (80 months from May 2001 to December 2007) CHAMP radio occultation data. Generally there a good agreement averaged over 300–10 for all years/seasons with global annual mean biases (standard deviations) less than 0.3 (1.7) K. Regional temporal discrepancies occur within polar vortex mainly on...
Contrary to optical images, Synthetic Aperture Radar (SAR) images are in different electromagnetic spectrum where the human visual system is not accustomed to. Thus, with more and SAR applications, demand for enhanced high-quality has increased considerably. However, entail high costs due limitations of current devices their image processing resources. To improve quality reduce generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) generate images. This method...