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

Automatic Road Extractions from High Resolution Satellite Imagery Using Road Intersection Model in Urban Areas

This paper proposes intersection model and strategy for road extraction from high resolution satellite images. Satellite images are rich in information. For Geographic Information System (GIS), many features require fast and reliable extraction of roads and intersections. They are also complex to analyze. Satellite image provides useful data that is extracted from satellite image of the urban area. Automatic extraction of the road intersections from the urban areas has been a challenging topic because the high resolution satellite images contain multiple layers that represent roads, buildings, and other high density objects. Our goals is to automatically separate the road layer from the other layers then extract the road intersections. Usually traditional image processing methods don't achieve satisfied performance in case of satellite images. This paper proposes a modified and a cost effective method for road extraction from high resolution satellites images. In order to find the precise road intersection of urban areas we have divided whole process into two sequential modules: first, extraction of road line using different Morphological direction filtering to automatically eliminate the other layers from road layer and finally, extraction of road intersections to determine the road orientation and interconnectivity. We applied this method to a set of randomly selected high resolution satellite image from urban and semi urban areas and the correctness of road network extraction reaches 95.71%, significantly higher than those of other existing road extraction methods

Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation

In light of the increasing availability of commercial high-resolution imaging sensors, automatic interpretation tools are needed to extract road features. Currently, many approaches for road extraction are available, but it is acknowledged that there is no single method that would be successful in extracting all types of roads from any remotely sensed imagery. In this paper, a novel classification of roads is proposed, based on both the roads' geometrical, radiometric properties and the characteristics of the sensors. Subsequently, a general road tracking framework is proposed, and one or more suitable road trackers are designed or combined for each type of roads. Extensive experiments are performed to extract roads from aerial/satellite imagery, and the results show that a combination strategy can automatically extract more than 60% of the total roads from very high resolution imagery such as QuickBird and DMC images, with a time-saving of approximately 20%, and acceptable spatial accuracy. It is proven that a combination of multiple algorithms is more reliable, more efficient and more robust for extracting road networks from multiple-source remotely sensed imagery than the individual algorithms

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Accurate, detailed and up-to-date road information is of special importance in geo-spatial databases as it is used in a variety of applications such as vehicle navigation, traffic management and advanced driver assistance systems (ADAS). The commercial road maps utilized for road navigation or the geographical information system (GIS) today are based on linear road centrelines represented in vector format with poly-lines (i.e., series of nodes and shape points, connected by segments), which present a serious lack of accuracy, contents, and completeness for their applicability at the sub-road level. For instance, the accuracy level of the present standard maps is around 5 to 20 meters. The roads/streets in the digital maps are represented as line segments rendered using different colours and widths. However, the widths of line segments do not necessarily represent the actual road widths accurately. Another problem with the existing road maps is that few precise sub-road details, such as lane markings and stop lines, are included, whereas such sub-road information is crucial for applications such as lane departure warning or lane-based vehicle navigation. Furthermore, the vast majority of roadmaps aremodelled in 2D space, whichmeans that some complex road scenes, such as overpasses and multi-level road systems, cannot be effectively represented. In addition, the lack of elevation information makes it infeasible to carry out applications such as driving simulation and 3D vehicle navigation

Geological Lineament Assessment from Passive and Active Remote Sensing Imageries

Lineament is any extensive linear feature on the Earth’s surface that can be identified when there is a change in the topographical data. The advancement of technologies in remote sensing and Geographical Information Sciences (GIS) lead to the various studies and methods in mapping lineaments due to the availability of data from small to large scale areas. Lineament can be extracted from remote sensing data either with manual, semi-automatic or automatic image processing techniques that incorporate in numerous remote sensing and GIS software. Manually digitizing or tracing the aerial photograph is a subjective method as the lineament will be interpreted based on geomorphological understanding in determining the possible relationship between the linear features. Therefore, this research proposed automatic lineaments extraction techniques that less time-consuming compared to the semi-automatic and manual approaches as the algorithms for lineament detection have been integrated in the software. The aim of this study is to compare multi-sensors active and passive remote sensing technologies of Landsat 8, Sentinel 1 and Sentinel 2 satellite data in lineament mapping, based on automatic image processing tools between the state boundaries of Selangor and Pahang in Peninsular Malaysia. Overall, statistics descriptions, density, and orientations analysis indicate a correlation between the extracted lineaments and the geology of the area. Furthermore, lineaments extracted from Sentinel 1 radar images show the most significant result. Actually, the accuracy assessment of matching lineaments provides the Sentinel 1 as the best sensor compared to both the Sentinel 2 and the Landsat 8, with root mean square errors (RMSE) equal to 1.660, 1.743 and 2.757, respectively. Therefore, both remote sensing technologies and geographical information sciences can be effectively integrated within the field of structural geology, thus allowing the mapping of lineaments in a more practical, cost and time-effective way

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin