• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1479-1488
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• 2022

Accidents in which oil spills occur intermittently in the ocean due to ship collisions and sinkings. In order to prepare prompt countermeasures when such an accident occurs, it is necessary to accurately identify the current status of spilled oil. To this end, the Coast Guard patrols the target area with a fixed-wing airplane or helicopter and checks it with the naked eye or video, but it was difficult to determine the area contaminated by the spilled oil and its exact location on the map. Accordingly, this study develops a technology for direct ortho-rectification by automatically geo-referencing aerial images collected by the Coast Guard without individual ground reference points to identify the current status of spilled oil. First, meta information required for georeferencing is extracted from a visualized screen of sensor information such as video by optical character recognition (OCR). Based on the extracted information, the external orientation parameters of the image are determined. Images are individually orthorectified using the determined the external orientation parameters. The accuracy of individual orthoimages generated through this method was evaluated to be about tens of meters up to 100 m. The accuracy level was reasonably acceptable considering the inherent errors of the position and attitude sensors, the inaccuracies in the internal orientation parameters such as camera focal length, without using no ground control points. It is judged to be an appropriate level for identifying the current status of spilled oil contaminated areas in the sea. In the future, if real-time transmission of images captured during flight becomes possible, individual orthoimages can be generated in real time through the proposed individual orthorectification technology. Based on this, it can be effectively used to quickly identify the current status of spilled oil contamination and establish countermeasures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.179-186
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• 2021

In general, the daily safety inspection activities, which investigate damages in structures and measures the size of the damage, have been relied heavily on the visual inspection so far. Since the probe of the condition and performance of facilities by such personnel is often dependent on the subjective judgment of the investigator, the consistency and repeatability of the probing results may reduce. Particularly, damage located in a difficult-to-reach place depends mainly on experience with the naked eye, and an unsafe method using a ladder has mainly applied when necessary. Therefore, in this study, we tried to propose a way of using images that can reduce the deviation between safety inspection investigators, enhance objectivity, and improve the safety of workers. In this study, we have applied homographic transformation as a method of correcting the image. As a result of analyzing the size of the damage in the corrected image of the test subject, it confirms that the accuracy of measuring the magnitude of the damage can satisfy the target levels of 5.0mm and 0.005m², the target accuracy levels. As a result of the field verification test to which the proposed image correction technique applied, the coefficient of variation of the crack length in the structure decreased from 5.4~7.0% to 0.072~0.12%, and that of the damaged area from 10.9% to 1.6%. It confirms that the measurement accuracy is improved. Therefore, it is expected that this study on the image utilization technique in safety inspection activities can increase the accuracy of damage measurement and improve the reliability of the safety inspection reports and exterior survey drawings.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.403-406
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• 2005

In order to evaluate the Capability of ETM+ remotely- sensed data to provide 'Forest-shrub land-Rangeland' cover type map in areas near the timberline of northern forests of Iran, the data were analyzed in a portion of nearly 790 ha located in Neka-Zalemroud region. First, ortho-rectification process was used to correct the geometric errors of the image, yielding 0/68 and 0/69 pixels of RMS. error in X and Y axis, respectively. The original and panchromatic bands were fused using PANSHARP Statistical module. The ground truth map was made using 1 ha field plots in a systematic-random sampling grid, and vegetative form of trees, shrubs and rangelands was recorded as a criteria to name the plots. A set of channels including original bands, NDVI and IR/R indices and first components of PCI from visible and infrared bands, was used for classification procedure. Pair-wise divergence through CHNSEL command was used, In order to evaluate the separability of classes and selection of optimal channels. Classification was performed using ML classifier, on both original and fused data sets. Showing the best results of $67\%$ of overall accuracy, and 0/43 of Kappa coefficient in original data set. Due to the results represented above, it's concluded that ETM+ data has an intermediate capability to fulfill the spectral variations of three form- based classes over the study area.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

• Smart Structures and Systems
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• v.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.