Automatic and semi-automatic extraction of curvilinear features from SAR images

Extraction of curvilinear features from synthetic aperture radar (SAR) images is important for automatic recognition of various targets, such as fences, surrounding the buildings. The bright pixels which constitute curvilinear features in SAR images are usually disrupted and also degraded by high amount of speckle noise which makes extraction of such curvilinear features very difficult. In this paper an approach for the extraction of curvilinear features from SAR images is presented. The proposed approach is based on searching the curvilinear features as an optimum unidirectional path crossing over the vertices of the features determined after a despeckling operation. The proposed method can be used in a semi-automatic mode if the user supplies the starting vertex or in an automatic mode otherwise. In the semi-automatic mode, the proposed method produces reasonably accurate real-time solutions for SAR images

High-resolution SAR images for fire susceptibility estimation in urban forestry

We present an adaptive system for the automatic assessment of both physical and anthropic fire impact factors on periurban forestries. The aim is to provide an integrated methodology exploiting a complex data structure built upon a multi resolution grid gathering historical land exploitation and meteorological data, records of human habits together with suitably segmented and interpreted high resolution X-SAR images, and several other information sources. The contribution of the model and its novelty rely mainly on the definition of a learning schema lifting different factors and aspects of fire causes, including physical, social and behavioural ones, to the design of a fire susceptibility map, of a specific urban forestry. The outcome is an integrated geospatial database providing an infrastructure that merges cartography, heterogeneous data and complex analysis, in so establishing a digital environment where users and tools are interactively connected in an efficient and flexible way

Extracting Physical and Environmental Information of Irish Roads Using Airborne and Mobile Sensors

Airborne sensors including LiDAR and digital cameras are now used extensively for capturing topographical information as these are often more economical and efficient as compared to the traditional photogrammetric and land surveying techniques. Data captured using airborne sensors can be used to extract 3D information important for, inter alia, city modelling, land use classification and urban planning. According to the EU noise directive (2002/49/EC), the National Road Authority (NRA) in Ireland is responsible for generating noise models for all roads which are used by more than 8,000 vehicles per day. Accordingly, the NRA has to cover approximately 4,000 km of road, 500m on each side. These noise models have to be updated every 5 years. Important inputs to noise model are digital terrain model (DTM), 3D building data, road width, road centre line, ground surface type and noise barriers. The objective of this research was to extract these objects and topographical information using nationally available datasets acquired from the Ordnance Survey of Ireland (OSI). The OSI uses ALS50-II LiDAR and ADS40 digital sensors for capturing ground information. Both sensors rely on direct georeferencing, minimizing the need for ground control points. Before exploiting the complementary nature of both datasets for information extraction, their planimetric and vertical accuracies were evaluated using independent ground control points. A new method was also developed for registration in case of any mismatch. DSMs from LiDAR and aerial images were used to find common points to determine the parameters of 2D conformal transformation. The developed method was also evaluated by the EuroSDR in a project which involved a number of partners. These measures were taken to ensure that the inputs to the noise model were of acceptable accuracy as recommended in the report (Assessment of Exposure to Noise, 2006) by the European Working Group. A combination of image classification techniques was used to extract information by the fusion of LiDAR and aerial images. The developed method has two phases, viz. object classification and object reconstruction. Buildings and vegetation were classified based on Normalized Difference Vegetation Index (NDVI) and a normalized digital surface model (nDSM). Holes in building segments were filled by object-oriented multiresolution segmentation. Vegetation that remained amongst buildings was classified using cues obtained from LiDAR. The short comings there in were overcome by developing an additional classification cue using multiple returns. The building extents were extracted and assigned a single height value generated from LiDAR nDSM. The extracted height was verified against the ground truth data acquired using terrestrial survey techniques. Vegetation was further classified into three categories, viz. trees, hedges and tree clusters based on shape parameter (for hedges) and distance from neighbouring trees (for clusters). The ground was classified into three surface types i.e. roads and parking area, exposed surface and grass. This was done using LiDAR intensity, NDVI and nDSM. Mobile Laser Scanning (MLS) data was used to extract walls and purpose built noise barriers, since these objects were not extractable from the available airborne sensor data. Principal Component Analysis (PCA) was used to filter points belonging to such objects. A line was then fitted to these points using robust least square fitting. The developed object extraction method was tested objectively in two independent areas namely the Test Area-1 and the Test Area-2. The results were thoroughly investigated by three different accuracy assessment methods using the OSI vector data. The acceptance of any developed method for commercial applications requires completeness and correctness values of 85% and 70% respectively. Accuracy measures obtained using the developed method of object extraction recommend its applicability for noise modellin

Avaliação da evolução do índice de vegetação de teledetecção usando de técnicas de processamento de imagens

Vegetation has a substantial role as an indicator of anthropic effects, specifically in cases where urban planning is required. This is especially the case in the management of coastal cities, where vegetation exerts several effects that heighten the quality of life (alleviation of unpleasant weather conditions, mitigation of erosion, aesthetics, among others). For this reason, there is an increased interest in the development of automated tools for studying the temporal and spatial evolution of the vegetation cover in wide urban areas, with an adequate spatial and temporal resolution. We present an automated image processing workflow for computing the variation of vegetation cover using any publicly available satellite imagery (ASTER, SPOT, LANDSAT, MODIS, among others) and a set of image processing algorithms specifically developed. The automatic processing methodology was developed to evaluate the spatial and temporal evolution of vegetation cover, including the Normalized Difference Vegetation Index (NDVI), the vegetation cover percentage and the vegetation variation. A prior urban area digitalization is required. The methodology was applied in Monte Hermoso city, Argentina. The vegetation cover per city block was computed and three transects over the city were outlined to evaluate the changes in NDVI values. This allows the computation of several information products, like NDVI profiles, vegetation variation assessment, and classification of city areas regarding vegetation. The information is available in GIS-readable formats, making it useful as support for urban planning decisions.A vegetação tem um papel importante como indicador de efeitos antrópicos, especificamente nos casos em que o planejamento urbano é necessário. Este é especialmente o caso na gestão de cidades costeiras, onde a vegetação exerce diversos efeitos que elevam a qualidade de vida (alívio de condições climáticas desagradáveis, mitigação da erosão, estética, entre outras). Por essa razão, há um interesse crescente no desenvolvimento de ferramentas automatizadas para o estudo da evolução temporal e espacial da cobertura vegetal em grandes áreas urbanas, com adequada resolução espacial e temporal. Apresentamos um fluxo de trabalho automatizado de processamento de imagens para calcular a variação da cobertura vegetal usando qualquer imagem de satélite publicamente disponível (ASTER, SPOT, LANDSAT, MODIS, entre outros) e um conjunto de algoritmos de processamento de imagem desenvolvidos especificamente. A metodologia de processamento automático foi desenvolvida para avaliar a evolução espacial e temporal da cobertura vegetal, incluindo o Índice de Vegetação da Diferença Normalizada (NDVI), o percentual de cobertura vegetal e a variação da vegetação. Uma digitalização prévia da área urbana foi necessária. A metodologia foi aplicada na cidade de Monte Hermoso, na Argentina. A cobertura vegetal por quarteirão foi computada e três transectos sobre a cidade foram delineados para avaliar as mudanças nos valores de NDVI. Isso permite o cálculo de vários produtos de informação, como perfis de NDVI, avaliação da variação da vegetação e classificação das áreas da cidade em relação à vegetação. A informação está disponível em formatos legíveis pelo GIS, tornando-a útil como suporte para decisões de planejamento urbano.Fil: Revollo Sarmiento, Natalia Veronica. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Revollo Sarmiento, Gisela Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Huamantinco Cisneros, María Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; ArgentinaFil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Piccolo, Maria Cintia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; Argentin

Uses and Challenges of Collecting LiDAR Data from a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices

Autonomous vehicles (AVs) that utilize LiDAR (Light Detection and Ranging) and other sensing technologies are becoming an inevitable part of transportation industry. Concurrently, transportation agencies are increasingly challenged with the management and tracking of large-scale highway asset inventory. LiDAR has become popular among transportation agencies for highway asset management given its advantage over traditional surveying methods. The affordability of LiDAR technology is increasing day by day. Given this, there will be substantial challenges and opportunities for the utilization of big data resulting from the growth of AVs with LiDAR. A proper understanding of the data size generated from this technology will help agencies in making decisions regarding storage, management, and transmission of the data. The original raw data generated from the sensor shrinks a lot after filtering and processing following the Cache county Road Manual and storing into ASPRS recommended (.las) file format. In this pilot study, it is found that while considering the road centerline as the vehicle trajectory larger portion of the data fall into the right of way section compared to the actual vehicle trajectory in Cache County, UT. And there is a positive relation between the data size and vehicle speed in terms of the travel lanes section given the nature of the selected highway environment