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

Abstract

Abstract: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 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 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 OPEN ACCESS Remote Sens. 2015, 7 8272 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.

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

“…Figure 10 illustrates the achieved improvement with respect to all three datasets. The dataset used in this experiment is a part of a scene that is recently used in the research works of Ameri et al [25], Miao et al [20] and Nikfar et al [18]. In Table 3 All methods fulfilled the lowest limits for completeness and correctness recommended in [8].…”

“…In road extraction from VHR images significant progress has been achieved by the remote sensing and photogrammetry community, e.g., [17,18,20,25,30], and by computer science researchers, e.g., [1,10,12,14,31]. Nevertheless, the ultimate goal of designing and implementing a reliable automatic road extraction system is still far-off [6,12,20,21,29].…”

“…The interested reader can find the details of the algorithm in [39,[41][42][43]. A successful implementation of this concept is the Trimble eCognition Developer software package, which catalyzed a boost of its exploitation in considerable research works [18,35,40,44,45].…”

“…In order to distinguish road segments from other segments their properties should be taken into account. The most dominant characteristics of roads in VHR images include: (1) roads are mostly elongated structures with bounded width [1,46,47]; (2) roads do not appear as a small segment or patch [10,13]; (3) usually the road surface is at least locally homogeneous [1,18,35]; and (4) roads have a limited and smoothly changing local curvature [48,49].…”

“…The dataset used in this experiment is a part of a scene that is recently used in the research works of Ameri et al [25], Miao et al [20] and Nikfar et al [18]. In Table 3 The dataset used in this experiment is a part of a scene that is recently used in the research works of Ameri et al [25], Miao et al [20] and Nikfar et al [18]. In Table 3 All methods fulfilled the lowest limits for completeness and correctness recommended in [8].…”