Automated geometric correction of multispectral images from High Resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B

China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral images from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites (CBERS-2 and CBERS-2B) are not precisely geo-referenced and orthorectified. The geometric accuracy of the HRCC multispectral image product is found to be within 2–11 km. The use of CBERS-HRCC multispectral images to monitor Earth surface dynamics therefore necessitates accurate geometric correction of these images. This paper presents an automated method for geo-referencing and orthorectifying the multispectral images from the HRCC imager on-board CBERS satellites. Landsat Thematic Mapper (TM) Level 1T (L1T) imagery provided by the U.S. Geological Survey (USGS) is employed as reference for geometric correction. The proposed method introduces geometric distortions in the reference image prior to registering it with the CBERS-HRCC image. The performance of the geometric correction method was quantitatively evaluated using a total of 100 images acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. The geometrically corrected HRCC images have an average geometric accuracy of 17.04 m (CBERS-2) and 16.34 m (CBERS-2B). While the applicability of the method for attaining sub-pixel geometric accuracy is demonstrated here using selected images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images.     

Pre-processing considerations of IRS-P6 LISS-4 imagery

Wide image swath with a high geometric resolution is required for photogrammetric applications. Both demands can be satisfied using staggered line arrays. Different bands of IRS-P6 LISS-4 sensor use staggered arrays for imaging. This paper describes a method for computing the offset for geometric alignment of odd and even lines of the staggered array of IRS-P6 LISS-4 imagery. The odd and even pixel rows are separated by 35 μm (equal to 5 pixels) in the focal plane in the along-track direction. Slightly different viewing angles of both lines of a staggered array can result in a variable sampling pattern on the ground because of the attitude fluctuations, satellite movement, terrain topography, PSM steering and small variations in the angular placement of the CCD lines (from the pre-launch values) in the focal plane. Non-accounting of this variable sampling value during the video data alignment will introduce deterioration of image quality and geometric discontinuity of features. The stagger parameters can be computed by the reconstruction of the viewing geometry with a calibrated camera geometry model and a public domain DEM. The impact of the line separation in the focal plane during imaging for different viewing configurations and terrain heights are studied and reported in this paper. Computed values from the model are in good agreement with what is observed in the raw image for different view angles. The results verify the model and are representative of the stability of the platform.     

Crowdsourcing geospatial data

In this paper we review recent developments of crowdsourcing geospatial data. While traditional mapping is nearly exclusively coordinated and often also carried out by large organisations, crowdsourcing geospatial data refers to generating a map using informal social networks and web 2.0 technology. Key differences are the fact that users lacking formal training in map making create the geospatial data themselves rather than relying on professional services; that potentially very large user groups collaborate voluntarily and often without financial compensation with the result that at a very low monetary cost open datasets become available and that mapping and change detection occur in real time. This situation is similar to that found in the Open Source software environment.We shortly explain the basic technology needed for crowdsourcing geospatial data, discuss the underlying concepts including quality issues and give some examples for this novel way of generating geospatial data. We also point at applications where alternatives do not exist such as life traffic information systems. Finally we explore the future of crowdsourcing geospatial data and give some concluding remarks.