This paper presents an automatic procedure for the geometric corrections of very-high resolution (VHR) optical panchromatic satellite images. The procedure is composed of three steps: an automatic ...ground control point (GCP) extraction algorithm that matches the linear features that were extracted from the satellite image and reference data; a geometric model that applies a rational function model; and, the orthorectification procedure. Accurate geometric corrections can only be achieved if GCPs are employed to precisely correct the geometric biases of images. Due to the high resolution and the varied acquisition geometry of images, we propose a fast, segmentation based method for feature extraction. The research focuses on densely populated urban areas, which are very challenging in terms of feature extraction and matching. The proposed algorithm is capable of achieving results with a root mean square error of approximately one pixel or better, on a test set of 14 panchromatic Pléiades images. The procedure is robust and it performs well in urban areas, even for images with high off-nadir angles.
The Sentinel-2 is a high resolution optical satellite mission, developed by the European Space Agency (ESA) for the European Commission. Currently the mission has two satellites in orbit: Sentinel-2A ...from 23rd June 2015, and Sentinel-2B from 7th March 2017. Revisit time for this twin constellation is five days. The twin satellites represent a very stable data source, but for the first six operational months (i.e. the second half of the 2015). From the very start of the mission, the Research Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU) is collecting its own archive of Sentinel-2 data for the extent of the Republic of Slovenia. The data is saved in its original processing state (Level-1C) and in four levels of processing. The archive has now been operating for six full years, which is a good milestone for a statistical overview. We analyse the archive of Sentinel-2 data of Slovenia from several aspects at the country level. A special focus is placed on cloud cover, as only cloud-free data can give an impression of the actual spatial and temporal usability of optical data.
In response to the increasing need for fast satellite image processing SPACE-SI developed STORM—a fully automatic image processing chain that performs all processing steps from the input optical ...images to web-delivered map-ready products for various sensors. This paper focuses on the automatic geometric corrections module and its adaptation to very high resolution (VHR) multispectral images. In the automatic ground control points (GCPs) extraction sub-module a two-step algorithm that utilizes vector roads as a reference layer and delivers GCPs for high resolution RapidEye images with near pixel accuracy was initially implemented. Super-fine positioning of individual GCPs onto an aerial orthophoto was introduced for VHR images. The enhanced algorithm is capable of achieving accuracy of approximately 1.5 pixels on WorldView-2 data. In the case of RapidEye images the accuracies of the physical sensor model reach sub-pixel values at independent check points. When compared to the reference national aerial orthophoto the accuracies of WorldView-2 orthoimages automatically produced with the rational function model reach near-pixel values. On a heterogeneous set of 41 RapidEye images the rate of automatic processing reached 97.6%. Image processing times remained under one hour for standard-size images of both sensor types.
When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. ...When normality is violated, the classification methods that rely on the assumption of normally distributed data are not as successful or accurate. It is hard to detect normality violations in small samples. The segmentation process produces segments that vary highly in size; samples can be very big or very small. This paper investigates whether the complexity within the segment can be addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures. In order to analyze the effect sampling has on classification results, statistics and probability value equations of non-parametric two-sample Kolmogorov-Smirnov test and parametric Student's t-test are selected as similarity measures in the classification process. The performance of both classifiers was assessed on a WorldView-2 image for four land cover classes (roads, buildings, grass and trees) and compared to two commonly used object-based classifiers-k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). Both proposed classifiers showed a slight improvement in the overall classification accuracies and produced more accurate classification maps when compared to the ground truth image.
Do podatkov Sentinel je, odvisno od misije in vrste podatkov, mogoče dostopati prek običajnih dostopnih točk ESE in EUMETSAT-a2 ali prek platform DIAS3 (Data and Information Access Services'), ki ...zagotavljajo (delno) zrcaljenje podatkov, tematskih platform TEP4 (Thematic Exploitation Platforms) ali regionalnih podatkovnih storitev5 (Copernicus Collaborative Ground Segment), kot tudi drugih uveljavl2 2 podatkovnih centrov6 ali spletišč za hitro prikazovanje in iskanje satelitskih podatkov po obširnih ? bazah podatkov teh centrov (med temi želimo posebej izpostaviti platformo Sentinel Hub, ki jo zelo uspešno upravlja slovensko podjetje Sinergise) (ESA Sentinel Data Access, 2021; ESA Earth Online, TS 2021). Medagencijski Odbor za satelite za opazovanje Zemlje (Committee on Earth Observation Satellites, CEOS) spodbuja pripravo in distribucijo tako imenovanih ,podatkov, pripravljenih za analizo' (Analysis Ready g Data, ARD), s čimer želi podpreti hitro, zanesljivo in avtomatizirano uporabo podatkov Sentinel ter ^ omogočiti interoperabilnost tako med različnimi obdobji zajema satelitskih podatkov kot z drugimi zbirkami satelitskih podatkov, na primer s podatki Landsat (CEOS, 2020). Doseganje ustrezne primerljivosti in boljše konsistentnosti satelitskih podatkov ter s tem boljše povezljivosti in združljivosti podatkov je zahtevna naloga, zato pri njej sodelujejo vsi pomembnejši proizvajalci podatkov, tj. vesoljske agencije (NASA, ESA, JAXA itd.) ter glavni centri za dostavo in dostop do podatkov (ponudniki storitev v oblaku, regionalna podatkovna vozlišča). ESIN portal Copernicus Open Access Hub (https://scihub.copernicus.eu/, prej znan kot Sentinels Scientific Data Hub) zagotavlja brezplačen in odprt dostop do uporabniških izdelkov Sentinel-1, Sentinel-2, Sentinel-3 in Sentinel-5P Prenos posnetkov s tega spletišča v naš arhiv smo realizirali prek vmesnika API Hub, ki
This article presents a review and comparison of the detection capability of water facilities or water surfaces with different systems of remote sensing: optical and radar satellite sensors, as well ...as optical sensors on aircraft. The capabilities of water detection are estimatedfrom several aspects: differences in the spatial and spectral resolution of imagery, the complexity of imagery pre-processing requirements, and the method of analysis and interpretation feasibilities for the type and purpose of mapping. Particular attention is paid to evaluating the applicability of remote sensing data in light of the detection efficiency of water areas in heterogeneously structured environments.
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