Peter Schwind

Modifications in the SIFT operator for effective SAR image matching

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 SIFTs 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 operators capability for point feature matching in SAR imagery.

Processors for ALOS Optical Data: Deconvolution, DEM Generation, Orthorectification, and Atmospheric Correction

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.

Bulk reprocessing of the ALOS PRISM/AVNIR-2 archive of the European Space Agency: Level 1 Orthorectified Data processing and Data Quality Evaluation

The Advanced Land Observing Satellite (ALOS) was launched on January 24, 2006, by a Japan Aerospace Exploration Agency (JAXA) H-IIA launcher. It carries three remote sensing sensors: the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2), the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), and the Phased Array type L-band Synthetic Aperture Radar (PALSAR). Within the framework of ALOS Data European Node (ADEN), as part of the European Space Agency (ESA), has collected 5 years of data observed in Arctic, in Europe and in Africa through the ground stations of Tromsoe (Norway) and Matera (Italy). Some data has been repatriated directly from JAXA from the on-board recorder (in particular over Africa, outside the visibility of the stations). The data were available to the scientific users via on-request ordering from the stations through the ESA ordering system. In ordering to provide a better and easier access to the data in the framework of the ESA Third Party Missions, in 2015 ESA started a project aimed to repatriate the data from the stations, consolidate them, harmonise the format to the ESA standards. For the PALSAR data, view the different processing levels available to the users, ESA decided to setup a dissemination system, able to process automatically at the user demand the data to the requested level (on-the-fly processing). For the optical data, instead, the decision was to systematically process the PRISM and AVNIR-2 as orthorectified products (so to a higher level in respect of what available before) with a systematic quality control. This paper presents the functionalities of the new Level 1 orthorectified products and details the block adjustment algorithms used for refinement of geometric accuracy. A specific quality control strategy has been laid down in order to re-analyse the entire archive. Also, validation methods are explained and the final product accuracy specification are given.

Using geometric accuracy of TerraSAR-X data for improvement of direct sensor orientation and ortho-rectification of optical sattelite data

The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. It is due to the fact that it measures distances which are mainly dependent on the position of the satellite and the terrain height. If the used DEM is of high accuracy, the resulting geocoded data are very precise. This precision can be used to improve the exterior orientation and thereby the geometric accuracy of optical satellite data. The technique used is the measurement of identical points in the images, either by manual measurements or through local image matching using mutual information and to estimate improvements for the attitude data through this information. By adjustment calculations falsely matched points can be eliminated and an optimal improvement can be found. The optical data are orthorectified using these improvements and the available DEM. The results are compared using conventional ground control information from GPS measurements.