N. Kerle

Object-Oriented Change Detection for Landslide Rapid Mapping

A complete multitemporal landslide inventory, ideally updated after each major event, is essential for quantitative landslide hazard assessment. However, traditional mapping methods, which rely on manual interpretation of aerial photographs and intensive field surveys, are time consuming and not efficient for generating such event-based inventories. In this letter, a semi-automatic approach based on object-oriented change detection for landslide rapid mapping and using very high resolution optical images is introduced. The usefulness of this methodology is demonstrated on the Messina landslide event in southern Italy that occurred on October 1, 2009. The algorithm was first developed in a training area of Altolia and subsequently tested without modifications in an independent area of Itala. Correctly detected were 198 newly triggered landslides, with user accuracies of 81.8% for the number of landslides and 75.9% for the extent of landslides. The principal novelties of this letter are as follows: 1) a fully automatic problem-specified multiscale optimization for image segmentation and 2) a multitemporal analysis at object level with several systemized spectral and textural measurements.

Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis

To detect landslides by object-based image analysis using criteria based on shape, color, texture, and, in particular, contextual information and process knowledge, candidate segments must be delineated properly. This has proved challenging in the past, since segments are mainly created using spectral and size criteria that are not consistent for landslides. This paper presents an approach to select objectively parameters for a region growing segmentation technique to outline landslides as individual segments and also addresses the scale dependence of landslides and false positives occurring in a natural landscape. Multiple scale parameters were determined using a plateau objective function derived from the spatial autocorrelation and intrasegment variance analysis, allowing for differently sized features to be identified. While a high-resolution Resourcesat-1 Linear Imaging and Self Scanning Sensor IV (5.8 m) multispectral image was used to create segments for landslide recognition, terrain curvature derived from a digital terrain model based on Cartosat-1 (2.5 m) data was used to create segments for subsequent landslide classification. Here, optimal segments were used in a knowledge-based classification approach with the thresholds of diagnostic parameters derived from If-means cluster analysis, to detect landslides of five different types, with an overall recognition accuracy of 76.9%. The approach, when tested in a geomorphologically dissimilar area, recognized landslides with an overall accuracy of 77.7%, without modification to the methodology. The multiscale classification-based segment optimization procedure was also able to reduce the error of commission significantly in comparison to a single-optimal-scale approach.

An ontology of slums for image - based classification

Abstract Information about rapidly changing slum areas may support the development of appropriate interventions by concerned authorities. Often, however, traditional data collection methods lack information on the spatial distribution of slum-dwellers. Remote sensing based methods could be used for a rapid inventory of the location and physical composition of slums. (Semi-)automatic detection of slums in image data is challenging, owing to the high variability in appearance and definitions across different contexts. This paper develops an ontological framework to conceptualize slums using input from 50 domain-experts covering 16 different countries. This generic slum ontology (GSO) comprises concepts identified at three levels that refer to the morphology of the built environment: the environs level, the settlement level and the object level. It serves as a comprehensive basis for image-based classification of slums, in particular, using object-oriented image analysis (OOA) techniques. This is demonstrated by with an example of local adaptation of GSO and OOA parameterization for a study area in Kisumu, Kenya. At the object level, building and road characteristics are major components of the ontology. At the settlement level, texture measures can be potentially used to represent the contrast between planned and unplanned settlements. At the environs level, factors which extend beyond the site itself are important indicators, e.g. hazards due to floods plains and marshy conditions. The GSO provides a comprehensive framework that includes all potentially relevant indicators that can be used for image-based slum identification. These characteristics may be different for other study areas, but show the applicability of the developed framework.

Automatic structural seismic damage assessment with airborne oblique pictometry imagery

Accurate and rapid mapping of seismic building damage is essential to support rescue forces and estimate economic losses. Traditional methods have limitations: ground-based mapping is slow and largely limited to facade information, and image-based mapping is typically restricted to vertical (roof) views. Here, we assess the value of photogrammetrically processed airborne oblique, multi-perspective Pictometry data, in a two-step approach: (a) supervised classification into facades, intact roofs, destroyed roofs and vegetation using 22 image-derived features, and (b) combining the classification results from different viewing directions into a per-building damage score adapted from the European Macroseismic Scale (EMS 98) for damage classification (no-moderate damage, heavy damage, destruction). Overall classification accuracies for the four classes and for the building damage of 70 percent and 63 percent, respectively, were achieved. Image stereo overlap helped classify facades, but problems with the relatively vague EMS damage class definitions were encountered, and subjectivity in training data generation affected overall classification by up to 10 percent.

Satellite-based damage mapping following the 2006 Indonesia earthquake : How accurate was it?

Abstract The Yogyakarta area in Indonesia suffered a devastating earthquake on 27 May 2006. There was an immediate international response, and the International Charter “Space and Major Disasters” was activated, leading to a rapid production of image-based damage maps and other assistance. Most of the acquired images were processed by UNOSAT and DLR-ZKI, while substantial damage mapping also occurred on the ground. This paper assesses the accuracy and completeness of the damage maps produced based on Charter data, using ground damage information collected during an extensive survey by Yogyakartas Gadjah Mada University in the weeks following the earthquake and that has recently become available. More than 54,000 buildings or their remains were surveyed, resulting in an exceptional validation database. The UNOSAT damage maps outlining clusters of severe damage are very accurate, while earlier, more detailed results underestimated damage and missed larger areas. Damage maps produced by DLR-ZKI, using a damage-grid approach, were found to underestimate the extent and severity of the devastation. Both mapping results also suffer from limited image coverage and extensive cloud contamination. The ground mapping gives a more accurate picture of the extent of the damage, but also illustrates the challenge of mapping a vast area. The paper concludes with a discussion on ways to improve Charter-based damage maps by integration of local knowledge, and to create a wider impact through generation of customised mapping products using web map services.

Active Learning in the Spatial Domain for Remote Sensing Image Classification

Active learning (AL) algorithms have been proven useful in reducing the number of required training samples for remote sensing applications; however, most methods query samples pointwise without considering spatial constraints on their distribution. This may often lead to a spatially dispersed distribution of training points unfavorable for visual image interpretation or field surveys. The aim of this study is to develop region-based AL heuristics to guide user attention toward a limited number of compact spatial batches rather than distributed points. The proposed query functions are based on a tree ensemble classifier and combine criteria of sample uncertainty and diversity to select regions of interest. Class imbalance, which is inherent to many remote sensing applications, is addressed through stratified bootstrap sampling. Empirical tests of the proposed methods are performed with multitemporal and multisensor satellite images capturing, in particular, sites recently affected by large-scale landslide events. The assessment includes an experimental evaluation of the labeling time required by the user and the computational runtime, and a sensitivity analysis of the main algorithm parameters. Region-based heuristics that consider sample uncertainty and diversity are found to outperform pointwise sampling and region-based methods that consider only uncertainty. Reference landslide inventories from five different experts enable a detailed assessment of the spatial distribution of remaining errors and the uncertainty of the reference data.

Transferability of object - oriented image analysis methods for slum identification

Updated spatial information on the dynamics of slums can be helpful to measure and evaluate progress of policies. Earlier studies have shown that semi-automatic detection of slums using remote sensing can be challenging considering the large variability in definition and appearance. In this study, we explored the potential of an object-oriented image analysis (OOA) method to detect slums, using very high resolution (VHR) imagery. This method integrated expert knowledge in the form of a local slum ontology. A set of image-based parameters was identified that was used for differentiating slums from non-slum areas in an OOA environment. The method was implemented on three subsets of the city of Ahmedabad, India. Results show that textural features such as entropy and contrast derived from a grey level co-occurrence matrix (GLCM) and the size of image segments are stable parameters for classification of built-up areas and the identification of slums. Relation with classified slum objects, in terms of enclosed by slums and relative border with slums was used to refine classification. The analysis on three different subsets showed final accuracies ranging from 47% to 68%. We conclude that our method produces useful results as it allows including location specific adaptation, whereas generically applicable rulesets for slums are still to be developed.

The Function of Remote Sensing in Support of Environmental Policy

Limited awareness of environmental remote sensing’s potential ability to support environmental policy development constrains the technology’s utilization. This paper reviews the potential of earth observation from the perspective of environmental policy. A literature review of “remote sensing and policy” revealed that while the number of publications in this field increased almost twice as rapidly as that of remote sensing literature as a whole (15.3 versus 8.8% yr−1), there is apparently little academic interest in the societal contribution of environmental remote sensing. This is because none of the more than 300 peer reviewed papers described actual policy support. This paper describes and discusses the potential, actual support, and limitations of earth observation with respect to supporting the various stages of environmental policy development. Examples are given of the use of remote sensing in problem identification and policy formulation, policy implementation, and policy control and evaluation. While initially, remote sensing contributed primarily to the identification of environmental problems and policy implementation, more recently, interest expanded to applications in policy control and evaluation. The paper concludes that the potential of earth observation to control and evaluate, and thus assess the efficiency and effectiveness of policy, offers the possibility of strengthening governance.

Effect of Sun Elevation Angle on DSMs Derived from Cartosat-1 Data

Along-track stereoscopic satellite data are increasingly used for automatic extraction of digital surface models (DSM) due to the reduced radiometric variation between the images. Problems remain with the quality of such DSMs, especially in steep terrain. This paper explores the accuracy of DSMs extracted from Cartosat-1 data acquired under high and low sun elevation angle conditions in High Himalayan terrain. The metric accuracy of the DSM was estimated by comparing it with check points obtained with a differential GPS . Additionally, we used spatial discrepancy of drainage lines to estimate errors in the DSM due to spatial auto- correlation. For valleys perpendicular to the satellite track, the DSM extracted from a low sun elevation angle data showed 45 percent higher spatial accuracy than the DSM extracted from high sun elevation angle data. The results indicate that the sun elevation angle and valley orientation affect the spatial accuracy of the DSM, though metric accuracy remains comparable.

Reviving Legacy Population Maps With Object-Oriented Image Processing Techniques

Vast archives of legacy maps exist for most parts of the world, containing analog information on a variety of environmental and socioeconomic parameters, and often dating back to the nineteenth century. The information contained in those data, which is potentially of great utility in environmental change or demographic studies, has traditionally only been accessible through digitizing or visual map analysis. In this paper, we show how object-oriented analysis can be used to unlock such information. We demonstrate this on a 1962 map of Kenya, which shows population distribution using differently sized dots. The procedure developed extracts those population signatures accurately, despite size and color variations, dot bleeding, and conglutination, as well as overlap with other map elements. Over 39 000 dots were automatically extracted, corresponding to 99.6% of the published population figure, with an accuracy between 94.6% and 98.5% for the different symbol sizes. We also discuss the utility of the derived geographic-information-system-ready information, for example, to assess malaria exposure and calculate population change figures, using the 1999 census data at a detailed sublocation level.