With increasing availability of satellite data of high temporal resolution, a more robust classifier is needed which can exploit the temporal information along with the spectral information of the remote sensing image...
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With increasing availability of satellite data of high temporal resolution, a more robust classifier is needed which can exploit the temporal information along with the spectral information of the remote sensing images. Specific fuzzy-based and learning-basedalgorithms are two broad categories and have the potential to perform well in spectral-temporal domain. In the present study, for mapping paddy fields as a specific class two classification algorithms, viz. fuzzy-based modified possibilistic c-mean (MPCM) algorithm and learning-based 1D-convolutional neural networks (CNN), were tested using Sentinel-2A/2B temporal data. The overall accuracy for learning-based 1D-CNN and fuzzy-based MPCM classifiers was found to be 96% and 93%, respectively. The F-measure values were found to be 0.95 and 0.92 for 1D-CNN- and MPCM-based classifier, respectively. Thus, it can be inferred from this study that the 1D-CNN classifier performed better than the traditional fuzzy-based classifier and can handle heterogeneity within class.
In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold E...
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ISBN:
(数字)9781510617421
ISBN:
(纸本)9781510617421
In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-basedalgorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.
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