Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery

Maryam Nikfar, Mohammad Zoej, Mehdi Mokhtarzade, Mahdi Shoorehdeli
2015 Remote Sensing  
The growing availability of high-resolution satellite imagery provides an opportunity for identifying road objects. Most studies associated with road detection are scene-related and also based on the digital number of each pixel. Because images can provide more details (including color, size, shape, and texture), object-based processing is more advantageous. Therefore, in this paper, to handle the existing uncertainty of satellite image pixel values, using type-2 fuzzy set theory in combination
more » ... with object-based image analysis is proposed. Because the main challenges of the type-2 fuzzy set are parameter tuning and extensive computations, a hybrid genetic algorithm (GA) consisting of Pittsburgh and cooperative-competitive learning schemes is proposed to address these problems. The most prominent feature of our research in this work is to establish a comprehensive object-based type-2 fuzzy logic system that enables us to detect roads in high-resolution satellite images with no training data. The validation assessment of road detection results using the proposed framework for independent images demonstrates the capability and efficiency of our method in identifying road objects. For more evaluation, a type-1 fuzzy OPEN ACCESS Remote Sens. 2015, 7 8272 logic system with the same structure as type-2 is tuned. Evaluations show that type-1 fuzzy logic system quality in training is very similar to that of the proposed type-2 fuzzy framework. However, in general, its lower accuracy, as inferred by validation assessments, makes the type-1 fuzzy logic system significantly different from the proposed type-2. Keywords: type-2 fuzzy sets; genetic cooperative-competitive learning; object-based image analysis; road detection; satellite imagery Remote Sens. 2015, 7 8273 Related work Mena presented a review of nearly 250 references on road extraction [5] . Starting from seed points, linear feature extraction methods that were developed using active contour models, called snakes, were presented by Laptev et al. and Gruen et al. [6, 7] . Doucette et al. presented an automated road centerline extraction method that exploits spectral content from high-resolution multi-spectral images [8] . The method is based on anti-parallel edge centerline extraction and self-organized road mapping. Zhang and Couloigner introduced a wavelet approach for road extraction from high-spatial-resolution remotely sensed imagery [9] . Valadanzoej and Mokhtarzadeh extracted roads using artificial neural networks that concentrated on evaluating different structures of neural networks, along with different measuring units and descriptors [10] . Peng et al. updated outdated road maps by incorporating generic and specific prior knowledge into a multi-scale phase field model [11] . Valero et al. detected road networks using directional mathematical morphology [12] . Movaghati et al. extracted roads using particle filtering (PF) and extended Kalman filtering (EKF). Starting from an initial point, the EKF module is responsible for tracing the median axis of a single road segment, whereas the PF module begins operating at road intersections [13] . A multi-stage strategy for automatically extracting roads from high-resolution multispectral satellite images based on salient features was introduced by Mirnalinee. This method incorporates the salient features of roads using a probabilistic support vector machine (P-SVM) and dominant singular measure (DSM) [14] . Shao et al. introduced a fast linear feature detector for road extraction. Only ridge line (or bright ribbon) extractions that are mostly roads in aerial and satellite images are considered in this paper [15] . Shi et al. proposed a method for extracting main road centerlines using path openings and closings as well as the support vector machine (SVM) classifier from images with a spatial resolution of 6 meters. In the proposed method, roads wider than four pixels are considered [16] . A method for accurate road centerline extraction from a classified image is proposed by Miao et al. [17] . Also, some new research based on SAR imagery and LIDAR system has been done in the last year [18] [19] [20] [21] . To overcome the shortcomings of the pixel-based methods and to reduce the semantic gap, region-based algorithms have been developed [1-3]. Region-based classification is known to achieve better results than pixel-based classification in processing HSR images [22] [23] [24] . A considerable number of studies have compared object-based approaches with traditional pixel-based classification methods [25] [26] [27] [28] [29] . Many of these studies have found that object-based methods typically produce higher classification accuracies than pixel-based methods do. A new work is proposed by Huang and Zhang based on SVM and a multi-feature model at both pixel and object levels [30, 31] . Zarinpanjeh et al. used object-based analysis for road map updates [32] . Additionally, Grote et al. developed a method for road network extraction using object-based analysis [33] . To reduce the effects of vagueness in road detection from satellite images, T1 FSs have been proposed in some literature. Agouris et al. used fuzzy logic for segmentation of an image. In this method, brighter pixels are considered more likely to be closer to real road pixels. Then, a template-matching algorithm is applied along the user-defined direction to locate the best road position [34]. A system for road extraction from IKONOS multispectral imagery based on fuzzy logic is proposed by Amini et al. [35] . Hinz and Wiedemann present an approach for self-diagnosis which is a part of an existing road extraction system. In their paper, fuzzy set theory is used as theoretical framework for knowledge representation for evaluation [36] . Bacher and Mayer extracted areas with
doi:10.3390/rs70708271 fatcat:efakvynimfcgfprxzayg4ghzl4