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With the increasing availability and rapidly improving the spatial resolution of synthetic aperture radar (SAR) images from the latest and future satellites like TerraSAR-X and TanDEM-X, their applicability in remote sensing applications is set to be paramount. Considering challenges in the field of point feature-based multisensor/multimodal SAR image matching/registration and advancements in the field of computer vision, we extend the applicability of the scale invariant feature transform (SIFT) operator for SAR images. In this article, we have analysed the feature detection, identification and matching steps of the original SIFT processing chain. We implement steps to counter the speckle influence, which deteriorates the SIFT operator performance for SAR images. In feature identification, we evaluate different local gradient estimating techniques and highlight the fact that giving up the SIFT's rotation invariance characteristic increases the potential number of matches when the multiple SAR images from different sensors have been acquired with the same geometrical acquisition parameters. In the feature matching stage, we propose to assist the standard SIFT matching scheme to utilise the SIFT operator capability for effective results in challenging SAR image matching scenarios. The results obtained for SAR images acquired by different sensors using different incidence angles and orbiting directions over both rural and semi urban land cover, highlight the SIFT operator's capability for point feature matching in SAR imagery.

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A rapid screening method for the detection of antiphospholipid antibodies is described. Dense, red dyed polystyrene beads coated with cardiolipin were incubated with test sera for a short period of time, then added to a microtube containing anti-human IgG in a gel provided within a pre-cast card (DiaMed ID Microtyping System). The card was centrifuged at 150g for 5 min and then examined for movement of the beads through the gel.

ABSTRACT:In this paper a method to improve the co-registration accuracy of two separate HySpex SWIR and VNIR cameras is proposed. The first step of the presented approach deals with the detection of point features from both scenes using the BRISK feature detector. After matching these features, the match coordinates in the VNIR scene are orthorectified and the resulting ground control points in the SWIR scene are filtered using a sensor-model based RANSAC. This implementation of RANSAC estimates the boresight angles of a scene by iteratively fitting the sensor-model to a subset of the matches. The boresight angles which can be applied to most of the remaining matches are then used to orthorectify the scene. Compared to previously used methods, the main advantages of this approach are the high robustness against outliers and the reduced runtime. The proposed methodology was evaluated using a test data set and it is shown in this work that the use of BRISK for feature detection followed by sensor-model based RANSAC significantly improves the co-registration accuracy of the imagery produced by the two HySpex sensors.

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The German Aerospace Center (DLR) is responsible for the development of prototype processors for PRISM and AVNIR-2 data under a contract of the European Space Agency. The PRISM processor comprises the radiometric correction, an optional deconvolution to improve image quality, the generation of a digital elevation model, and orthorectification. The AVNIR-2 processor comprises radiometric correction, orthorectification, and atmospheric correction over land. Here, we present the methodologies applied during these processing steps as well as the results achieved using the processors.

The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric correction software, capable of processing data acquired by many different optical satellite sensors. Based on this well established algorithm, a new Python-based atmospheric correction software has been developed to generate L2A products of Sentinel-2, Landsat-8, and of new space-based hyperspectral sensors such as DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program). This paper outlines the underlying algorithms of PACO, and presents the validation results by comparing L2A products generated from Sentinel-2 L1C images with in situ (AERONET and RadCalNet) data within VNIR-SWIR spectral wavelengths range.