ABSTRACT:Identification of street light poles is very significant and crucial for intelligent transportation systems. Automatic detection and extraction of street light poles are a challenging task in road scenes. This is mainly because of complex road scenes. Nowadays mobile laser scanners have been used to acquire three-dimensional geospatial data of roadways over a large area at a normal driving speed. With respect to the high density of such data, new and beneficial algorithms are needed to extract objects from these data. In this article, our proposed algorithm for extraction of street light poles consists of five main steps: 1. Preprocessing, 2. Ground removal, 3. 3D connected components analysis, 4. Local geometric feature generation, 5. Extraction of street light poles using Bhattacharya distance metric. The proposed algorithm is tested on two rural roadways, called Area1 and Area2. Evaluation results for Area1 report 0.80, 0.72 and 0.62 for completeness, correctness and quality, respectively. * talebi@ut.ac.ir

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ABSTRACT:In the most applications in remote sensing, there is no need to use all of available data, such as using all of bands in hyperspectral images. In this paper, a new band selection method was proposed to deal with the large number of hyperspectral images bands. We proposed a Continuous Genetic Algorithm (CGA) to achieve the best subset of hyperspectral images bands, without decreasing Overall Accuracy (OA) index in classification. In the proposed CGA, a multi-class SVM was used as a classifier. Comparing results achieved by the CGA with those achieved by the Binary GA (BGA) shows better performances in the proposed CGA method. At the end, 56 bands were selected as the best bands for classification with OA of 78.5 %.*

Mobile lidar scanning is one of the recent technologies that is used to map street scenes rapidly. Among street objects, utility‐poles are more critical to energy companies to monitor regularly through time. This paper presents a novel approach to detect utility‐poles from mobile lidar data in complex city scenes. After removing ground points, the scene is gridded into blocks based on a shared‐partitioning algorithm. Next, an interwoven column generation algorithm is used to create columns. Finally, each of these columns is considered to be a utility‐pole or not. The proposed algorithm is tested on two test areas. The algorithm achieved Completeness, Correctness and Quality of 92.8%, 97.5% and 90.6% in Area 1, and 92.8%, 92.2% and 86.1% in Area 2. The total number of utility‐poles in both areas was 265. The algorithm shows promising results in utility‐pole detection in complex city scenes with attached walls.

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