The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Bahareh Kalantar - One of the best experts on this subject based on the ideXlab platform.
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optimized Rule based flood mapping technique using multitemporal radarsat 2 images in the tropical region
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Biswajeet Pradhan, Maher Ibrahim Sameen, Bahareh KalantarAbstract:Flood is one of the most common natural disasters in Malaysia. Preparing an accurate flood inventory map is the basic step in flood risk management. Flood detection is a complex process because of the limitation of methodological approaches and cloud coverage over tropical areas. An efficient approach is proposed to identify flooded areas using multitemporal RADARSAT-2 imageries. First, multispectral Landsat image was used to extract and subtract permanent water bodies, and this image was later utilized to extract the same information from multitemporal RADARSAT-2 imageries. Next, water bodies during a flood event were extracted from RADARSAT-2 images. Permanent water bodies, shadow, and paddy were detected from synthetic aperture radar (SAR) images by analyzing their temporal backscattering values. During feature Extraction, Rule-based object-oriented technique was applied to classify both SAR and Landsat images. Image segmentation during object-based analysis was performed to distinguish the boundaries of various dimensions and scales of objects. Moreover, a Taguchi-based method was employed to optimize the segmentation parameters. After segmentation, the Rules were defined and images were classified to produce an accurate flood inventory map for the 2014 Kelantan flood. A confusion matrix was generated to evaluate the performance of the classification method. The overall accuracy of 86.16% was achieved for RADARSAT-2 using Rule-based classification and optimization technique. The resulting flood inventory map using the proposed approach supported the efficiency of the proposed methodology.
Biswajeet Pradhan - One of the best experts on this subject based on the ideXlab platform.
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optimized Rule based flood mapping technique using multitemporal radarsat 2 images in the tropical region
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Biswajeet Pradhan, Maher Ibrahim Sameen, Bahareh KalantarAbstract:Flood is one of the most common natural disasters in Malaysia. Preparing an accurate flood inventory map is the basic step in flood risk management. Flood detection is a complex process because of the limitation of methodological approaches and cloud coverage over tropical areas. An efficient approach is proposed to identify flooded areas using multitemporal RADARSAT-2 imageries. First, multispectral Landsat image was used to extract and subtract permanent water bodies, and this image was later utilized to extract the same information from multitemporal RADARSAT-2 imageries. Next, water bodies during a flood event were extracted from RADARSAT-2 images. Permanent water bodies, shadow, and paddy were detected from synthetic aperture radar (SAR) images by analyzing their temporal backscattering values. During feature Extraction, Rule-based object-oriented technique was applied to classify both SAR and Landsat images. Image segmentation during object-based analysis was performed to distinguish the boundaries of various dimensions and scales of objects. Moreover, a Taguchi-based method was employed to optimize the segmentation parameters. After segmentation, the Rules were defined and images were classified to produce an accurate flood inventory map for the 2014 Kelantan flood. A confusion matrix was generated to evaluate the performance of the classification method. The overall accuracy of 86.16% was achieved for RADARSAT-2 using Rule-based classification and optimization technique. The resulting flood inventory map using the proposed approach supported the efficiency of the proposed methodology.
Yanliu Liu - One of the best experts on this subject based on the ideXlab platform.
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A Method of Web Information Automatic Extraction Based on XML
2016Co-Authors: Jie Song, Na Zhang, Yanliu LiuAbstract:Abstract. With the increasingly high-speed of the internet as well as the increase in the amount of data it contains, users are finding it more and more difficult to gain useful information from the web. How to extract accurate information from the Web efficiently has become an urgent problem. Web information Extraction technology has emerged to solve this kind of problem. The method of Web information auto-Extraction based on XML is designed through standardizing the HTML document using data translation algorism, forming an extracting Rule base by learning the XPath expression of samples, and using Extraction Rule base to realize auto-Extraction of pages of same kind. The results show that this approach should lead to a higher recall ratio and precision ratio, and the result should have a self-description, making it convenient for founding data Extraction system of each domain
Maher Ibrahim Sameen - One of the best experts on this subject based on the ideXlab platform.
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optimized Rule based flood mapping technique using multitemporal radarsat 2 images in the tropical region
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Biswajeet Pradhan, Maher Ibrahim Sameen, Bahareh KalantarAbstract:Flood is one of the most common natural disasters in Malaysia. Preparing an accurate flood inventory map is the basic step in flood risk management. Flood detection is a complex process because of the limitation of methodological approaches and cloud coverage over tropical areas. An efficient approach is proposed to identify flooded areas using multitemporal RADARSAT-2 imageries. First, multispectral Landsat image was used to extract and subtract permanent water bodies, and this image was later utilized to extract the same information from multitemporal RADARSAT-2 imageries. Next, water bodies during a flood event were extracted from RADARSAT-2 images. Permanent water bodies, shadow, and paddy were detected from synthetic aperture radar (SAR) images by analyzing their temporal backscattering values. During feature Extraction, Rule-based object-oriented technique was applied to classify both SAR and Landsat images. Image segmentation during object-based analysis was performed to distinguish the boundaries of various dimensions and scales of objects. Moreover, a Taguchi-based method was employed to optimize the segmentation parameters. After segmentation, the Rules were defined and images were classified to produce an accurate flood inventory map for the 2014 Kelantan flood. A confusion matrix was generated to evaluate the performance of the classification method. The overall accuracy of 86.16% was achieved for RADARSAT-2 using Rule-based classification and optimization technique. The resulting flood inventory map using the proposed approach supported the efficiency of the proposed methodology.
Shaochen Lui - One of the best experts on this subject based on the ideXlab platform.
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iepad information Extraction based on pattern discovery
The Web Conference, 2001Co-Authors: Chiahui Chang, Shaochen LuiAbstract:research in information Extraction (IE) regards the generation of wrappers that can extract particular information from semi- structured Web documents. Similar to compiler generation, the extractor is actually a driver program, which is accompanied with the generated Extraction Rule. Previous work in this field aims to learn Extraction Rules from users' training example. In this paper, we propose IEPAD, a system that automatically discovers Extraction Rules from Web pages. The system can automatically identify record boundary by repeated pattern mining and multiple sequence alignment. The discovery of repeated patterns are realized through a data structure call PAT trees. Additionally, repeated patterns are further extended by pattern alignment to comprehend all record instances. This new track to IE involves no human effort and content-dependent heuristics. Experimental results show that the constructed Extraction Rules can achieve 97 percent Extraction over fourteen popular search engines.
