Description: Urban areas are often characterized by geometrically simple and repetitive patterns, in particular, in cases where settlements have been built-up from scratch in a well-planned manner, e.g., according to architectonic, economic, or sociopolitic constraints. This leads to preferred rectangular and regular alignment of objects like windows or balconies at façades for the majority of buildings in modern cities. In this paper, we show how this regularity can be exploited for the challenging task of synthetic aperture radar (SAR) scene description and demonstrate the applicability in case studies. We present a virtually parameter-free method to segment a persistent scatterer point cloud and fit optimal lattices to describe separate façades. Formulating the PS as nodes in a graph allows us to use spectral graph theory to distinguish lattices even when they are overlapping or disturbed due to layover. As a result, we obtain an object-based representation of the SAR data, which allows for many new applications in the field of building monitoring and change detection in urban areas.

Description: In this paper, a methodology which allows automated and efficient reconstruction of three-dimensional (3-D) geometric building models from an Airborne Laser Scanning (ALS) point cloud is introduced and its performance is analyzed and evaluated. The proposed method avoids abnormal and/or infinite solutions which are typically encountered in previously published methods that use the rooftop primitive adjacency matrix to solve the critical rooftop vertices. In particular, first, an improved random sample consensus (RANSAC) algorithm is proposed to segment the rooftop primitives, i.e., the planar patches that constitute rooftops, of each building or group of connected buildings. The algorithm successfully maintains topological consistency among primitives and avoids under- and over-segmentation with high efficiency. Second, a novel Voronoi-based primitive boundary extraction algorithm under constraints of outer and inner building boundaries is introduced in order to extract each primitive boundary. In this algorithm, the adjacent segmented primitive relationships among the various primitives are preserved by a subgraph of the Voronoi diagram so that the reconstructed neighbor primitives are seamlessly connected. Third, in order to refine the boundary shapes of primitives with irregular geometry, various criteria for making the boundary adjustments more effective are proposed. In this way, more regular 3-D buildings can be produced. Finally, the primitive boundary simplification criteria are formally introduced to generate compact 3-D building models. By using the simplification criteria, nonadjacency between neighbor primitives, intersection between boundaries, and self-intersections are, to a great extent, avoided. Numerous experimental results obtained using multiple data sets, including data from the cities of Toronto and Enschede as well as from the Niagara area, have shown that the proposed methodology has excellent performance and it can produce watertight 3-D po- yhedral building models.

Description: In this work, we present a new semi-supervised strategy for obtaining finer spatial resolution urban maps from coarser resolution satellite data. Our method first uses a coarse resolution map as a source of training data. Then, we use semi-supervised learning in order to refine the set of initial (labeled) training samples by the inclusion of additional (reliable) unlabeled samples at the finer resolution level, in fully automatic fashion. The new unlabeled samples are automatically generated by our proposed methodology, which only requires a limited number of initial labeled samples for initialization purposes. Then, we conduct land cover classification (at the finer spatial resolution level) using a probabilistic multinomial logistic regression (MLR) classifier—in both supervised and semi-supervised fashion—by considering different numbers of labeled and unlabeled samples. In order to exploit spatial information, we use a Markov random field (MRF)-based postprocessing strategy to refine the obtained classification results. In order to test our concept, we use a global dataset: the European Space Agency’s GlobCover product, as the coarser resolution map (300-m spatial resolution). Our experimental evaluation is further conducted using Landsat data (30-m spatial resolution) collected over three different locations in the city of Sao Paulo, Brazil, and over two different locations in the city of Guangzhou, China. We obtain promising results in the generation of finer resolution urban extent maps using very limited training samples, derived in all cases from the GlobCover product. These experiments suggest the potential of GlobCover to provide reliable training data in order to support mapping of urban areas at a global scale.

Description: In remote sensing change detection, Markov random field (MRF) has been used successfully to model the prior probability using class-labels dependencies. MRF has played an important role in the detection of complex urban changes using optical images. However, the preservation of details in urban change analysis turns out to be a highly complex task if multitemporal SAR images with their speckle are to be used. Here, the ability of MRF to preserve geometric details and to combat speckle effect at the same time becomes questionable. Blob-region phenomenon and fine structures removal are common consequences of the application of traditional MRF-based change detection algorithm. To overcome these limitations, the iterated conditional modes (ICM) framework for the optimization of the maximum a posteriori (MAP-MRF) criterion function is extended to include a nonlocal probability maximization step. This probability model, which characterizes the relationship between pixels’ class-labels in a nonlocal scale, has the potential to preserve spatial details and to reduce speckle effects. Two multitemporal SAR datasets were used to assess the proposed algorithm. Experimental results using three density functions [i.e., the log normal (LN), generalized Gaussian (GG), and normal distributions (ND)] have demonstrated the efficiency of the proposed approach in terms of detail preservation and noise suppression. Compared with the traditional MRF algorithm, the proposed approach proved to be less-sensitive to the value of the contextual parameter and the chosen density function. The proposed approach has also shown less sensitivity to the quality of the initial change map when compared with the ICM algorithm.

Description: Water is a fundamental element in urban environments, and water body extraction is important for landscape and urban planning. Remote sensing has increasingly been used for water body extraction; however, in urban environments, this kind of approaches is challenging because of the significant within-class spectral variance in water areas and the presence of complex ground features. The objective of this study is to develop an automatic method that could improve water body extraction in urban environments from moderate spatial resolution satellite images. Central to our method is the combined use of multiscale extractions and spectral mixture analysis techniques in adaptive local regions. Specifically, we first calculate the NDWI image from experimental images for selecting water sample pixels. Second, on the basis of the selected water pixels, we apply an improved spectral mixture analysis technique on the experimental image to get water abundance of every pixel, and segment the abundance image to extract water bodies at the global scale. Third, in a similar manner, we iteratively conduct the water body extraction in multiscale local regions to refine the water bodies. Finally, the final result of water bodies is obtained when a stopping criterion is satisfied. We have implemented this method to produce water maps from an ALOS/AVNIR-2 image and a Terra/ASTER image covering urban areas. The experimental results illustrate that the proposed method has substantially outperformed two related methods that use the NDWI-based thresholding and the SVM classification for the entire image.

Description: In this paper, we present a novel combination of object features to both match buildings from predisaster images to shapes in a postdisaster image and assess damage on those buildings. These features include scale profile ratios extracted from a tree of shapes representation of the original image as well as texture features. A supervised classifier is used to classify building damage into three representative classes tied to the European Macroseismic Scale (EMS-98). The method is compared to visual inspection results as well as other automated methods. Results clearly show the benefits of our method for fast crisis mapping applications with few human inputs required.

Description: Some performance evaluation systems for building extraction techniques are manual in the sense that only visual results are provided or human judgment is employed. Many evaluation systems that employ one or more thresholds to ascertain whether an extracted building or roof plane is correct are subjective and cannot be applied in general. There are only a small number of automatic and threshold-free evaluation systems, but these do not necessarily consider all special cases, e.g., when over- and under-segmentation occurs during the extraction of roof planes. This paper proposes an automatic and threshold-free evaluation system that offers robust object-based evaluation of building extraction techniques. It makes one-to-one correspondences between extracted and reference entities using the maximum overlaps. Its application to the evaluation of a building extraction technique shows that it estimates different performance indicators including segmentation errors. Consequently, it can be employed for bias-free evaluation of other techniques whose outputs consist of polygonal entities.

Description: A fully polarimetric synthetic aperture radar (PolSAR) image allows the generation of a number of polarimetric descriptors. These descriptors are sensitive to changes in land use and cover. Thus, the objective of this study is twofold: first, to identify the most effective descriptors for each change type and ascertain the best complementary pairs from the selected polarimetric descriptors; and second, to develop an information fusion approach to use the unique features found in each polarimetric descriptor to obtain a better change map for urban and suburban environments. The effectiveness of each descriptor was assessed through statistical analysis of the sensitivity index in selected areas and through change detection results obtained by using the supervised thresholding method. A good agreement was found between the statistical analysis and the performance of each descriptor. Finally, a polarimetric information fusion method based on the coupling of modified thresholding with a region-growing algorithm was implemented for the identified complementary descriptor pairs. The mapping accuracy, as measured by the Kappa coefficient, was improved by 0.09 (from 0.76 to 0.85) with a significant reduction of false and missing alarm rates compared to using single PolSAR images.

Description: Airborne Lidar (Light detection and ranging) data have been widely used for classifying different land cover types. However, few researchers have conducted urban land cover classification using discrete airborne Lidar data as the sole data source. This research explores the possibility of applying airborne Lidar data to land cover classification in urban areas. The elevation difference and intensity difference between the first and last return, which may not work efficiently in pixel-based classification, were employed as two key attributes at the object level. Since tree objects have a much larger proportion of returns which show the elevation and intensity difference, the two indicators were used to classify the most indistinguishable land cover types, buildings and trees. In addition, height and intensity information were integrated to classify other land cover types. A case study was conducted in the city of Cambridge and eight urban land cover types were classified with an overall accuracy of 93.6%. Each land cover type was classified with an accuracy of between 80% and 100% and among these types, the accuracy of more than 90% for trees and buildings was satisfactory.