The measurement noise and sensor faults are likely to be stochastically unrelated, while event measurements are likely to be spatially correlated. The Bayesian detection scheme in [14] selects the minimum neighbors for a given detection error boundary such that the communication volume is minimized during the fault correction. Luo et al. in [14] did not explicitly attempt to detect faulty sensors, instead the schemes they proposed improve the event detection accuracy in the presence of faulty sensors.Article [15] presents a distributed fault detection algorithm for wireless sensor networks. Each sensor node identifies its own status based on
The increasing availability of commercial high-resolution satellite imaging sensors such as SPOT5, IKONOS, QuickBird and TerraSAR, requires the availability of suitable automatic interpretation tools to extract and identify cartographic features, especially in rapidly changing urban areas.

Roads are one of the most important linear cartographic features. Particularly, extraction of road networks from remotely sensed imagery is not only meaningful for cartography and topography [1], but also significant for various applications of geodata such as automatically aligning two spatial datasets [2] or automated vehicle navigation [3]. Therefore, research on the automatic extraction of road networks from remotely sensed imagery has been a topical research theme in the various fields of photogrammetry, remote sensing, geographic information systems, pattern recognition, and computer vision.

As a result, many strategies, methodologies and algorithms for road network extraction have been presented since the 1970s, which have achieved varying degrees of success [4]. According to the level of automation, Cilengitide the techniques for road extraction with the aid of computer vision can be coarsely classified into automatic and semi-automatic approaches.The automatic methods attempt to seek an analysis and interpretation of the image similar to that of a human operator. Nevatia and Babu [5] utilized an edge detection method to identify ribbon roads with lateral and parallel anti-edges. Radon transform was employed to locate the roadsides and to measure the width of a road [6]. Due to the variation of the complexity of image contents, the above low level edge detection methods are insufficient to extract the road features with high completeness, correctness, quality and accuracy. Therefore, more high level techniques have been developed. For example, angular texture signatures can make use of the characteristics of road’s texture, and this is utilized to find candidate road centerline points [7] or to discriminate road surfaces from the parking lots [8].