Precise high-resolution Digital Elevation Models (DEMs) are essential for creation of terrain relief and associated terrain hazard area maps, urban land development, smart cities and in other applications. The 3D modelling system entitled the UCL Co-registration Ames Stereo Pipeline (ASP) Gotcha Optimised (CASP-GO) was demonstrated on stereo data of Mars to generate 3D models for around 20% of Martian surface using cloud computers which was reported in 2018. CASP-GO is an automated DEM/DTM processing chain for NASA Mars, lunar and Earth Observation data including Mars 6m Context Camera (CTX) and High Resolution Imaging Science Experiment (HiRISE) 25cm stereo-data as well as ASTER 18m stereo data acquired on the NASA EOS Terra platform. CASP-GO uses tie-point based multi- resolution image co-registration, combined with sub-pixel refinement and densification. It is based on a combination of the NASA ASP and an adaptive least squares cor- relation and region growing matcher called Gotcha (Gruen-Otto-Chau). CASP-GO was successfully applied to produce more than 5300 DTMs of Mars (http://www.i- Mars.eu/web-GIS). This work employs CASP-GO to obtain DEMs from high resolution Earth Observation (EO) satellite video system SSTL Carbonite-2. CASP- GO was modified to work with multi-view point-and-stare video data including subpixel fusion of point clouds. Multi-view stereo video data are distinguished from still image data by a richer amount of information and noisier water areas.
The time series of various parameters of satellite imagery (NDVI/EVI, temperature) during the growing season were
considered in this work. This means that satellite images were considered not like a number of single scenes but like
temporal sequences. Using time series enables estimating the integral phenological properties of vegetation. The basis of
the developed technique is to use one of the methods of transformation of the multidimensional space in order to get the
principal components. The technique is based on considering each dimension of the multidimensional space as satellite
imagery for a specific date range. The technique automatically identifies spatial patterns of vegetation that are similar by
phenology and growing conditions. Subsequent analysis allowed identification of the belonging of derived classes.
Thus, the technique of revealing the spatial distribution of different dynamical vegetation patterns based on the
phenological characteristics has been developed. The technique is based on a transformation of the multidimensional
space of states of vegetation. Based on the developed technique, areas were obtained with similar interannual trends.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.