Extração de vias combinando métodos de detecção de regiões e linhas em imagens de intensidade de pulso laser

This paper aims at extracting street centerlines from previously isolated street regions by using the image of laser scanning intensity. In this image, streets are easily identified, since they manifest as dark, elongate ribbons contrasting with background objects. The intensity image is segmented by using the region growing technique, which generates regions representing the streets. From these regions, the street centerlines are extracted in two manners. The first one is through the Steger lines detection method combined with a line length thresholding by which lines being shorter than a minimum length are removed. The other manner is by combining the skeletonization method of regions based on the Medial Axis Transform and with a pruning process to eliminate as much as possible the ramifications. Experiments showed that the Steger-based method provided results better than the method based on skeletonization.Este trabalho tem por objetivo extrair os eixos de vias a partir de regiões de vias isoladas previamente, tendo por base uma imagem de intensidade de pulso laser. Neste tipo de imagem, as vias são facilmente identificadas, pois se apresentam como faixas alongadas e em tonalidade bem escura e homogênea, contrastando com os outros objetos da imagem. A imagem de intensidade é segmentada através da técnica de crescimento de regiões, gerando regiões que representam as vias. A partir dessas regiões, os eixos das vias são extraídos de duas formas. Uma delas combina o método de detecção de linhas de Steger com um procedimento de limiarização por comprimento das linhas, visando a reduzir a ocorrência de linhas espúrias. A outra se baseia no método de esqueletização de regiões pela Transformada do Eixo Médio, seguido de um processo de poda para eliminar o máximo possível as ramificações. A partir dos experimentos realizados é possível comparar os eixos extraídos pelas duas formas descritas, e aquela que utiliza o método de detecção de linhas de Steger apresenta melhores resultados do que a baseada no método de esqueletização

Building roof contour extraction from LiDAR data

This paper proposes a method for the automatic extraction of building roof contours from a LiDAR-derived digital surface model (DSM). The method is based on two steps. First, to detect aboveground objects (buildings, trees, etc.), the DSM is segmented through a recursive splitting technique followed by a region merging process. Vectorization and polygonization are used to obtain polyline representations of the detected aboveground objects. Second, building roof contours are identified from among the aboveground objects by optimizing a Markov-random-field-based energy function that embodies roof contour attributes and spatial constraints. Preliminary results have shown that the proposed methodology works properly

Extração de vias combinando métodos de detecção de regiões e linhas em imagens de intensidade de pulso laser

This paper aims at extracting street centerlines from previously isolated street regions by using the image of laser scanning intensity. In this image, streets are easily identified, since they manifest as dark, elongate ribbons contrasting with background objects. The intensity image is segmented by using the region growing technique, which generates regions representing the streets. From these regions, the street centerlines are extracted in two manners. The first one is through the Steger lines detection method combined with a line length thresholding by which lines being shorter than a minimum length are removed. The other manner is by combining the skeletonization method of regions based on the Medial Axis Transform and with a pruning process to eliminate as much as possible the ramifications. Experiments showed that the Steger-based method provided results better than the method based on skeletonization.Este trabalho tem por objetivo extrair os eixos de vias a partir de regiões de vias isoladas previamente, tendo por base uma imagem de intensidade de pulso laser. Neste tipo de imagem, as vias são facilmente identificadas, pois se apresentam como faixas alongadas e em tonalidade bem escura e homogênea, contrastando com os outros objetos da imagem. A imagem de intensidade é segmentada através da técnica de crescimento de regiões, gerando regiões que representam as vias. A partir dessas regiões, os eixos das vias são extraídos de duas formas. Uma delas combina o método de detecção de linhas de Steger com um procedimento de limiarização por comprimento das linhas, visando a reduzir a ocorrência de linhas espúrias. A outra se baseia no método de esqueletização de regiões pela Transformada do Eixo Médio, seguido de um processo de poda para eliminar o máximo possível as ramificações. A partir dos experimentos realizados é possível comparar os eixos extraídos pelas duas formas descritas, e aquela que utiliza o método de detecção de linhas de Steger apresenta melhores resultados do que a baseada no método de esqueletização

Automatic extraction of road seeds from high-resolution aerial images

This article presents an automatic methodology for extraction of road seeds from high-resolution aerial images. The method is based on a set of four road objects and another set of connection rules among road objects. Each road object is a local representation of an approximately straight road fragment and its construction is based on a combination of polygons describing all relevant image edges, according to some rules embodying road knowledge. Each one of the road seeds is composed by a sequence of connected road objects, in which each sequence of this type can be geometrically structured as a chain of contiguous quadrilaterals. Experiments carried out with high-resolution aerial images showed that the proposed methodology is very promising in extracting road seeds. This article presents the fundamentals of the method and the experimental results, as well

Canny-EDP detector: A Combination between Canny and non-linear anisotropic diffusion theories

This paper proposes a methodology for edge detection in digital images using the Canny detector, but associated to a previous edge structure focusing by a non-linear anisotropic diffusion via Partial Differential Equation (PDE). This strategy aims at minimizing the effect of the well-know duality of Canny detector, by which is not possible to simultaneously enhance the insensitiveness to image noises and the localization precision of detected edges. The process of anisotropic diffusion via PDE is used to previously focus the edge structure due to its notable characteristic in selectively smoothing the image, leaving the homogeneous regions strongly smoothed and mainly preserving the physical edges, i.e., those that are really related to objects presented on the image. The solution for the mentioned duality consists in applying the Canny detector in fine gaussian scale but only along the edge regions focused by the process of anisotropic diffusion via PDE. The obtained results showed that the method is appropriated for application involving automatic feature extraction, as it allowed the high-precision localization of thinned edges, which are usually related to objects presented on the image

Semiautomatic object-space road extraction combining a stereoscopic image pair and a TIN-Based DTM

In this letter, a semiautomatic method for road extraction in object space is proposed that combines a stereoscopic pair of low-resolution aerial images with a digital terrain model (DTM) structured as a triangulated irregular network (TIN). First, we formulate an objective function in the object space to allow the modeling of roads in 3-D. In this model, the TIN-based DTM allows the search for the optimal polyline to be restricted along a narrow band that is overlaid upon it. Finally, the optimal polyline for each road is obtained by optimizing the objective function using the dynamic programming optimization algorithm. A few seed points need to be supplied by an operator. To evaluate the performance of the proposed method, a set of experiments was designed using two stereoscopic pairs of low-resolution aerial images and a TIN-based DTM with an average resolution of 1 m. The experimental results showed that the proposed method worked properly, even when faced with anomalies along roads, such as obstructions caused by shadows and trees

Snake-Based Model for Automatic Roof Boundary Extraction in the Object Space Integrating a High-Resolution Aerial Images Stereo Pair and 3D Roof Models

The accelerated urban development over the last decades has made it necessary to update spatial information rapidly and constantly. Therefore, cities’ three-dimensional models have been widely used as a study base for various urban problems. However, although many efforts have been made to develop new building extraction methods, reliable and automatic extraction is still a major challenge for the remote sensing and computer vision communities, mainly due to the complexity and variability of urban scenes. This paper presents a method to extract building roof boundaries in the object space by integrating a high-resolution aerial images stereo pair, three-dimensional roof models reconstructed from light detection and ranging (LiDAR) data, and contextual information of the scenes involved. The proposed method focuses on overcoming three types of common problems that can disturb the automatic roof extraction in the urban environment: perspective occlusions caused by high buildings, occlusions caused by vegetation covering the roof, and shadows that are adjacent to the roofs, which can be misinterpreted as roof edges. For this, an improved Snake-based mathematical model is developed considering the radiometric and geometric properties of roofs to represent the roof boundary in the image space. A new approach for calculating the corner response and a shadow compensation factor was added to the model. The created model is then adapted to represent the boundaries in the object space considering a stereo pair of aerial images. Finally, the optimal polyline, representing a selected roof boundary, is obtained by optimizing the proposed Snake-based model using a dynamic programming (DP) approach considering the contextual information of the scene. The results showed that the proposed method works properly in boundary extraction of roofs with occlusion and shadows areas, presenting completeness and correctness average values above 90%, RMSE average values below 0.5 m for E and N components, and below 1 m for H component