Frank Canters

Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles

Meter to submeter resolution satellite images have generated new interests in extracting man-made structures in the urban area. However, classification accuracies for such purposes are far from satisfactory. Spectral characteristics of urban land cover classes are so similar that they cannot be separated using only spectral information. As a result, there is an increased interest in incorporating geometrical information. One possible approach is the use of morphological profiles (MPs). In this paper, we introduce two improvements on the use of MPs. Current approaches use disk-shaped structuring elements (SEs) to derive an MP. This profile contains information about the minimum dimension of objects. In this paper, we extend this approach by using linear SEs. This results in a profile containing information about the maximum object dimension. We show that the addition of the line-based MP gives a substantial improvement of the classification result. A second improvement is achieved by using ldquopartial morphological reconstructionrdquo instead of the normal morphological reconstruction. Morphological reconstruction is commonly used to better preserve the shape of objects. However, we show that this leads to ldquoover-reconstructionrdquo in typical remote sensing images and a decreased classification performance. With ldquopartial reconstruction,rdquo we are able to overcome this problem and still preserve the shape of objects.

A System-based Paradigm of Drought Analysis for Operational Management

Conventionally droughts are studied in terms of their dimensions (severity, duration and areal extent), without specifying the affected system. The paper presents an innovative system-based approach for drought analysis, which can lead to rational decisions for combating drought. Concepts of water scarcity (drought, water shortage, aridity and desertification) are viewed within the perspective of this new approach. The paper focuses also on operational water management in the presence of drought. Starting from the needs for such management, the affected system is defined and the related quantities are identified. Also, sub-systems are considered which allow the establishment of the link between specific variables and drought. Some drought characterisation methods are particularly suited for the systemic approach. Finally drought is considered as a natural hazard phenomenon and its consequences are discussed. Each physical sub-system can be improved by a variety of measures aiming at decreasing its vulnerability towards drought, so that the drought risk is mitigated. It is concluded that the clear definition of the affected system on the spatial and temporal scales can significantly contribute to the rational management for combating drought.

Fully Automatic Subpixel Image Registration of Multiangle CHRIS/Proba Data

Subpixel image registration is the key to successful image fusion and superresolution enhancement of multiangle satellite data. Multiangle image registration poses two main challenges: 1) Images captured at large view angles are susceptible to resolution change and blurring, and 2) local geometric distortion caused by topographic effects and/or platform instability may be important. In this paper, we propose a two-step nonrigid automatic registration scheme for multiangle satellite images. In the first step, control points (CPs) are selected in a preregistration process based on the scale-invariant feature transform (SIFT). However, the number of CPs obtained in this first step may be too few and/or CPs may be unevenly distributed. To remediate these problems, in a second step, the preliminary registered image is subdivided into chips of 64 × 64 pixels, and each chip is matched with a corresponding chip in the reference image using normalized cross correlation (NCC). By doing so, more CPs with better spatial distribution are obtained. Two criteria are applied during the generation of CPs to identify outliers. Selected SIFT and NCC CPs are used for defining a nonrigid thin-plate-spline model. The proposed registration scheme has been tested using data from the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (Proba) satellite. Experimental results demonstrate that the proposed method works well in areas with little variation in topography. Application in areas with more pronounced relief would require the use of orthorectified image data in order to achieve subpixel registration accuracy.

Assessing effects of input uncertainty in structural landscape classification

This paper presents the results of a study aimed at assessing the effects of input uncertainty on the outcome of a raster-based model for structural landscape classification. The model uses a DEM and a land-cover map as input, and calculates four structural indices from these data. The first two indices determine the openness of the landscape, the other two determine the degree of landscape homogeneity. By combining both aspects, nine different landscape types are defined. Applying Monte Carlo simulation, the effect of DEM error, uncertainty in land-cover classification, and the combined effect of both sources of uncertainty on the outcome of the landscape model are assessed. Special attention is paid to the spatial structure of uncertainty in both data sources.

Multiple Endmember Unmixing of CHRIS/Proba Imagery for Mapping Impervious Surfaces in Urban and Suburban Environments

In this paper, the potential of Compact High-Resolution Imaging Spectrometer (CHRIS)/Project for On-Board Autonomy data for impervious surface mapping is tested in a mixed urban/suburban/rural environment including part of the city of Leuven (Belgium) using multiple endmember unmixing. Various unmixing scenarios are compared, using different threshold values for the RMSE criterion applied to select the proper model for unmixing each pixel. Validation based on 25-cm aerial photography shows that the use of threshold values that favor the application of models with a small number of endmembers performs better compared to scenarios that make use of models with more endmembers. Detailed analysis of model selection for pixels with different land-cover composition indicates that the error in fraction estimation is partly related to spectral confusion between impervious surface types and bare soil, leading to the selection of inappropriate models for the unmixing. In spite of the spectral similarity of soil and impervious surface endmembers, average fractional error for impervious surfaces, vegetation, and bare soil is around 15%, which demonstrates the potential of CHRIS data for mapping the major physical components of the urban/suburban environment at the subpixel scale.

Comparing Different Approaches for Mapping Urban Vegetation Cover from Landsat ETM+ Data: A Case Study on Brussels

Urban growth and its related environmental problems call for sustainable urban management policies to safeguard the quality of urban environments. Vegetation plays an important part in this as it provides ecological, social, health and economic benefits to a citys inhabitants. Remotely sensed data are of great value to monitor urban green and despite the clear advantages of contemporary high resolution images, the benefits of medium resolution data should not be discarded. The objective of this research was to estimate fractional vegetation cover from a Landsat ETM+ image with sub-pixel classification, and to compare accuracies obtained with multiple stepwise regression analysis, linear spectral unmixing and multi-layer perceptrons (MLP) at the level of meaningful urban spatial entities. Despite the small, but nevertheless statistically significant differences at pixel level between the alternative approaches, the spatial pattern of vegetation cover and estimation errors is clearly distinctive at neighbourhood level. At this spatially aggregated level, a simple regression model appears to attain sufficient accuracy. For mapping at a spatially more detailed level, the MLP seems to be the most appropriate choice. Brightness normalisation only appeared to affect the linear models, especially the linear spectral unmixing.

Assessing urbanisation effects on rainfall-runoff using a remote sensing supported modelling strategy

Abstract This paper aims at developing a methodology for assessing urban dynamics in urban catchments and the related impact on hydrology. Using a multi-temporal remote sensing supported hydrological modelling approach an improved simulation of runoff for urban areas is targeted. A time-series of five medium resolution urban masks and corresponding sub-pixel sealed surface proportions maps was generated from Landsat and SPOT imagery. The consistency of the urban mask and sealed surface proportion time-series was imposed through an urban change trajectory analysis. The physically based rainfall-runoff model WetSpa was successfully adapted for integration of remote sensing derived information of detailed urban land use and sealed surface characteristics. A first scenario compares the original land-use class based approach for hydrological parameterisation with a remote sensing sub-pixel based approach. A second scenario assesses the impact of urban growth on hydrology. Study area is the Tolka River basin in Dublin, Ireland. The grid-based approach of WetSpa enables an optimal use of the spatially distributed properties of remote sensing derived input. Though change trajectory analysis remains little used in urban studies it is shown to be of utmost importance in case of time series analysis. The analysis enabled to assign a rational trajectory to 99% of all pixels. The study showed that consistent remote sensing derived land-use maps are preferred over alternative sources (such as CORINE) to avoid over-estimation errors, interpretation inconsistencies and assure enough spatial detail for urban studies. Scenario 1 reveals that both the class and remote sensing sub-pixel based approaches are able to simulate discharges at the catchment outlet in an equally satisfactory way, but the sub-pixel approach yields considerably higher peak discharges. The result confirms the importance of detailed information on the sealed surface proportion for hydrological simulations in urbanised catchments. In addition a major advantage with respect to hydrological parameterisation using remote sensing is the fact that it is site- and period-specific. Regarding the assessment of the impact of urbanisation (scenario 2) the hydrological simulations revealed that the steady urban growth in the Tolka basin between 1988 and 2006 had a considerable impact on peak discharges. Additionally, the hydrological response is quicker as a result of urbanisation. Spatially distributed surface runoff maps identify the zones with high runoff production. It is evident that this type of information is important for urban water management and decision makers. The results of the remote sensing supported modelling approach do not only indicate increased volumes due to urbanisation, but also identifies the locations where the most relevant impacts took place.

Superresolution Enhancement of Hyperspectral CHRIS/Proba Images With a Thin-Plate Spline Nonrigid Transform Model

Given the hyperspectral-oriented waveband configuration of multiangular CHRIS/Proba imagery, the scope of its application could widen if the present 18-m resolution would be improved. The multiangular images of CHRIS could be used as input for superresolution (SR) image reconstruction. A critical procedure in SR is an accurate registration of the low-resolution images. Conventional methods based on affine transformation may not be effective given the local geometric distortion in high off-nadir angular images. This paper examines the use of a nonrigid transform to improve the result of a nonuniform interpolation and deconvolution SR method. A scale-invariant feature transform is used to collect control points (CPs). To ensure the quality of CPs, a rigorous screening procedure is designed: 1) an ambiguity test; 2) the m-estimator sample consensus method; and 3) an iterative method using statistical characteristics of the distribution of random errors. A thin-plate spline (TPS) nonrigid transform is then used for the registration. The proposed registration method is examined with a Delaunay triangulation-based nonuniform interpolation and reconstruction SR method. Our results show that the TPS nonrigid transform allows accurate registration of angular images. SR results obtained from simulated LR images are evaluated using three quantitative measures, namely, relative mean-square error, structural similarity, and edge stability. Compared to the SR methods that use an affine transform, our proposed method performs better with all three evaluation measures. With a higher level of spatial detail, SR-enhanced CHRIS images might be more effective than the original data in various applications.

An Operational Superresolution Approach for Multi-Temporal and Multi-Angle Remotely Sensed Imagery

In this paper we propose an operational superresolution (SR) approach for multi-temporal and multi-angle remote sensing imagery. The method consists of two stages: registration and reconstruction. In the registration stage a hybrid patch-based registration scheme that can account for local geometric distortion and photometric disparity is proposed. Obstacles like clouds or cloud shadows are detected as part of the registration process. For the reconstruction stage a SR reconstruction model composed of the L1 norm data fidelity and total variation (TV) regularization is defined, with its reconstruction object function being efficiently solved by the steepest descent method. Other SR methods can be easily incorporated in the proposed framework as well. The proposed algorithms are tested with multi-temporal and multi-angle WorldView-2 imagery. Experimental results demonstrate the effectiveness of the proposed approach.

Quantifying uncertainty in remote sensing-based urban land-use mapping

Abstract Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use maps uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.