Use of multi-angle high-resolution imagery and 3D information for urban land-cover classification: a case study on Istanbul

The BELSPO-MAMUD project focuses on the use of Remote Sensing data for measuring and modelling urban dynamics. Remote sensing is a wonderful tool to produce long time-series of high resolution maps of sealed surface useful for this purpose. In the urban context of Istanbul, a very dynamic city, recent high resolution satellite images and medium resolution images from the past have been exploited to calibrate and validate a regression-based sub-pixel classification method allowing this production. In this context it’s a tricky task for several reasons: prominent occurrence of shadowed and occluded areas and urban canyons, spectral confusions between urban and non-urban materials at ground and roof levels, moderately hilly relief ... To cope with these difficulties the combined use of three types of data may be helpful: diachronic (i), multi-angle and 3D data. A master multispectral and panchromatic QuickBird image and a panchromatic Ikonos stereopair, all acquired in March 2002, were used in combination with a multispectral and panchromatic Ikonos image of May 2005. A DSM was generated from the Ikonos stereopair and building vector file. It was used for orthorectification, building height estimation and classification procedure. The area covered by the high resolution products was divided in 3 partitions and each one was classified independently. This application demonstrates that recent high resolution land-cover classification produced using multi-date, multi-angle and DSM can be used to produce sealed surface maps from longer timeseries of medium resolution images over large urban areas enabling so the analysis of urban dynamics

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

High resolution satellite imagery orientation accuracy assessment by leave-one-out method: accuracy index selection and accuracy uncertainty

The Leave-one-out cross-validation (LOOCV) was recently applied to the evaluation of High Resolution Satellite Imagery orientation accuracy and it has proven to be an effective method alternative with respect to the most common Hold-out-validation (HOV), in which ground points are split into two sets, Ground Control Points used for the orientation model estimation and Check Points used for the model accuracy assessment. On the contrary, the LOOCV applied to HRSI implies the iterative application of the orientationmodel using all the known ground points as GCPs except one, different in each iteration, used as a CP. In every iteration the residual between imagery derived coordinates with respect to CP coordinates (prediction error of the model on CP coordinates) is calculated; the overall spatial accuracy achievable from the oriented image may be estimated by computing the usual RMSE or, better, a robust accuracy index like the mAD (median Absolute Deviation) of prediction errors on all the iterations. In this way it is possible to overcome some drawbacks of the HOV: LOOCVis a reliable and robustmethod, not dependent on a particular set of CPs and on possible outliers, and it allows us to use each known ground point both as a GCP and as a CP, capitalising all the available ground information. This is a crucial problem in current situations, when the number of GCPs to be collected must be reduced as much as possible for obvious budget problems. The fundamentalmatter to deal with was to assess howwell LOOCVindexes (mADand RMSE) are able to represent the overall accuracy, that is howmuch they are stable and close to the corresponding HOV RMSE assumed as reference. Anyway, in the first tests the indexes comparison was performed in a qualitative way, neglecting their uncertainty. In this work the analysis has been refined on the basis of Monte Carlo simulations, starting from the actual accuracy of ground points and images coordinates, estimating the desired accuracy indexes (e.g. mAD and RMSE) in several trials, computing their uncertainty (standard deviation) and accounting for them in the comparison. Tests were performed on a QuickBird Basic image implementing an ad hoc procedure within the SISAR software developed by the Geodesy and Geomatics Team at the Sapienza University of Rome. The LOOCV method with accuracy evaluated by mAD seemed promising and useful for practical case

Influence of camera distortions on satellite image registration and change detection applications

Applications such as change detection and digital elevation model extraction from optical images require a rigorous modeling of the acquisition geometry. We show that the unrecorded satellite jitter during image acquisition, and the uncertainties on the CCD arrays geometry are the current major limiting factors for applications requiring high accuracy. These artifacts are identified and quantified on several optical satellites, i.e., SPOT, ASTER, QuickBird, and HiRISE

A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps

The Self-OrganizingMap (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topologypreservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topologypreservation, particularly using Kohonen's model. In this work, two methods for measuring the topologypreservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving ma