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Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise unsupervised (UND). SSND can be seen as an unbalanced classification between labeled and unlabeled samples using the Cost-Sensitive Support Vector...
In this paper we have analyzed military and security requirements for surveillance. Using a data-set of wide swath SAR imagery for the English channel and high resolution SAR data for the Zeebrugge and Rotterdam harbour we have discussed if these requirements can be met by using this kind of data. The requirement to detect anomalous behaviour at open sea can be fulfilled for ships with sizes >...
In this paper, the response of 7 Earth observation satellite sensors from multiple platforms, as well as different sensors on the same platform, are cross-compared over the Dome C plateau, Antarctica, during the austral summer of 2008–2009. A single, unified methodology is employed to remove geometric, temporal and atmospheric biases between different sensors with a range of spatial resolutions, bandwidths...
Post-launch calibration and characterization of Geostationary Earth Observing (GEO) satellite sensors, which lack on-board visible calibration, is a keenly felt need in many applications for studying long-term global climate changes. This paper proposes a vicarious technique of calibrating GEO visible sensors for the Clouds and the Earth's Radiant Energy System (CERES) project, using a kernel-based...
An extended superresolution observation model is proposed for POCS superresolution of hyperspectral images. Multiple constraint criteria based on a priori knowledge were incorporated: data consistence, amplitude constraint, Total Variation edge smoothing constraint, outlier rejection, and PCA based denoising. The constraint criteria are applied using POCS superresolution reconstruction. The method...
The preservation and accessibility of digital data and of their associated knowledge is today a very challenging and critical item in Europe. Earth Observation data are unique by nature and are fundamental for the monitoring of our environment and planet and of its changes. They are considered [1] as humankind asset and as such need to be preserved without time constrains and kept accessible together...
Remotely sensed observations in the visible to the shortwave infrared (VSWIR) and thermal infrared (TIR) regions of the electromagnetic spectrum can be used synergistically to provide valuable products of land surface properties for reliable assessments of carbon and water fluxes. The high spatial, spectral and temporal resolution VSWIR and TIR observations provided by the proposed Hyperspectral -...
Several studies suggest that the use of geometric features along with spectral information improves the classification and visualization quality of hyperspectral imagery. These studies normally make use of spatial neighborhoods of hyperspectral pixels for extracting these geometric features. In this work, we merge point cloud Light Detection and Ranging (LiDAR) data and hyperspectral imagery (HSI)...
The challenges of future global food production are characterized by a growing population, changing eating habits and climate change. Ecological intensification of global agriculture can avoid food shortage sustainably and preserve a minimum of nature. The paper describes logic and possible architecture of a global agricultural land management information system (ALMIS) as a means to implement ecological...
Inversion of radiative transfer models using a lookup-table (LUT) approach against hyperspectral data streams leads to retrievals of biophysical parameters such as chlorophyll content (Chl), but necessary optimization strategies are not consolidated yet. Here, various regularization options have been evaluated to the benefit of improved Chl retrieval from hyperspectral CHRIS data, being: i) the role...
The world's forests generate oxygen and store carbon, mitigating global climate change. Monitoring the health of these forests is an international priority, which benefits from the use of remote sensing. With hyperspectral sensors capable of discerning forest species and foliar chemistry, many forest information products can be generated including maps of forest species, canopy chemistry, biomass,...
In this paper the results of an additional campaign in the context of Flashing Fields are presented showing the impact of soil surface roughness on the directional backscattering. For the characterization of soil surface roughness a photogrametric measurement device was used and roughness was measured simultaneous to SAR observation made by ERS-2 and TerraSAR-X. As this study mainly confirmed and...
Low frequency synthetic-aperture radar (SAR) in combination with change detection techniques can be used to search for stationary ground targets. In particular, the method has shown a great potential when vehicle-size objects are obscured by foliage. However. one possibility is to improve the detection capabilities to increase the aperture angle of the radar data acquisition to make sure that the...
ESA's strong involvement in Education, Training and Capacity Building for Remote Sensing from Space, includes a plethora of activities, ranging from training courses, workshops and other events addressed to schools, universities and professionals, to special publications (e.g. atlases and teacher's packs), on-line material and educational software development. These activities are realized within...
Over the last two decades, the repeated Interferometric Synthetic Aperture Radar (InSAR) has proven to be useful for accurate topographic mapping and ground-surface motion monitoring. However, atmospheric delay variations are one of main errors in SAR interferograms. Since the GPS stations are sparse and the water vapor product of multispectral sensors is sensitive to the presence of clouds in the...
This paper deals with the problem of hyperspectral unmixing. We investigate the behavior of non-negative least squares (NNLS) as well as sparse ℓ1 unmixing and show that while the NNLS method does not take noise into account, the ℓ1 approach is biased towards smaller abundances and lower contrast. The application of the adaptive inverse scale space method, which we originally developed for compressed...
Impervious surface which is almost never affected by seasonal variations is a key indicator of urban expansion and urban heat island effect. In this study, we employ a new integrated enhancing index to extract impervious surfaces and compare the results with those retrieved by other existing spectral indices. The results show that the new integrated enhancing index can mitigate background noises effectively...
This paper investigates the usage of Johansen Cointegration Test for adaptive target detection with hyperspectral remote sensing data. Johansen Cointegration Test aims at mining long-term equilibrium relationship, which refers to the condition that if pairs of non-stationary series share similar tendencies, their linear combination could be stationary. Hyperspectral data are highly non-stationary...
Segmentation is a low-level operation extremely important for the extraction of information from digital images. A commonly adopted approach for the development of segmentation algorithms is that based on statistical modeling of data. For SAR images, the precise knowledge of the statistical properties and the adoption of an appropriate modeling are considered essential to obtain reliable results....
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