• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.4
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• pp.333-341
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• 2008

A remote sensing technique to identify trajectories of breaking waves in the surf zone using oblique digital nearshore videos is proposed. The noise arising from white foam induced by wave breaking has hindered accurate remote sensing of wave properties in the surf zone. For this reason, this paper focuses on image processing to remove the noise and wave trajectory identification essential for wave property estimation. The nearshore video imagery sampled at 3 Hz are used, covering length scale(100 m). Original image sequences are processed through image frame differencing and directional low-pass image filtering to remove the noise characterized by high frequencies in the video imagery. The extraction of individual wave crest features is conducted using a Radon transform-based line detection algorithm in the processed cross-shore image timestacks having a two-dimensional space-time domain. The number of valid wave crest trajectories identified corresponds to about 2/3 of waves recorded by the in-situ sensors.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.697-699
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• 2003

We present a simple algorithm for detection of red patches by remote sensing in coastal waters of Korea. The red tide patches can by identified by the relative intensity of red band signal with respect to the blue-green background signal, provided the radiometric signals only in the sea area are properly stretched. We tested our algorithm by Ocean COlor Monitor(OCM) data of Indian Satellite IRS-P4, which has been received from 2001 by National Fisheries Research and Development Institute of Korea. A comparison of our results with observation shows that the locations of red tides derived from remote sending imagery by our algorithm are in accordance with observations.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.162-164
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• 2003

The multi-disciplinary research project Strengthening Local Authorities in Risk Management (SLARIM), initiated by ITC, includes three case study cities in Asia. An important question is: what are the essential data for risk management and how to access such data. The role of common sources (e.g. census data), data derived from remote sensing (high-resolution satellite imagery, aerial photos), and data from close sensing (field observation, including mobile GIS) to acquire essential risk management data will be discussed. Special attention is given to the question of the minimum area and to disaggregating population data. A few examples are given of Kathmandu / Lalitpur, Nepal.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.423-423
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• 2004

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1412-1412
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• 2003

• Korean Journal of Remote Sensing
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• v.11 no.3
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• pp.101-116
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• 1995

The fact that the demands on traffic data collection are imposed by economic and safety considerations raisese the question of the potential for complementing existing traffic data collection programs with satellite data. Evaluating and monitoring traffic characteristics is becoming increasingly important as worsening congestion, declining economic situations, and increasing environmental sensitivies are forcing the government and municipalities to make better use of existing roadway capacities. The present system of using automatic counters at selected points on highways works well from a temporal point of view (i.e., during a specific period of time at one location). However, the present system does not cover the spatial aspects of the entire road system (i.e., for every location during specific periods of time); the counters are employed only at points and only on selected highways. This lack of spatial coverage is due, in part, to the cost of the automatic counters systems (fixed procurement and maintenance costs) and of the personal required to deploy them. The current procedure is believed to work fairly well in the aggregate mode, at the macro level. However, at micro level, the numbers are more suspect. In addition, the statistics only work when assuming a certain homogenity among characteristics of highways in the same class, an assumption that is impossible to test whn little or no data is gathered on many of the highways for a given class. In this paper, a remote sensing system as complement of the existing system is considered and implemented. Since satellite imagery with high resolution is not available, digitized panchromatic imagery acquired from an aircraft platform is utilized for initial test of the feasibility and performance capability of remote sensing data. Different levels of imagery resolutions are evaluated in an attempt to determine what vehicle types could be classified and counted against a background of pavement types, which might be expected in panchromatic satellite imagery. The results of a systematic study with three different levels of resolutions (1m, 2m and 4m) show that the panchromat ic reflectances of vehicles and pavements would be distributed so similarly that it would be difficult to classify systematically and analytically remotely sensing vehicles on pavement within panchromatic range. Anaysis of the aerial photographs show that the shadows of the vehicles could be a cue for vehicle detection.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.71-84
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• 2001

The geographical phenomena in space are well observed in the specific scale. This scale is called the operational scale. For an analysis of the optimum scale, it is needed to measure and represent the characteristics of attribute information extracted from the satellite imagery. The development of remote sensing technique makes various images with different resolution available. Researchers can select the image with optimum resolution for their analysis among various resolutions. For an effective analysis of the scale characteristics of satellite image, we investigated the characteristics of attribute information extracted from satellite image with different resolution. The two stage-procedure for exploring the optimum resolution proposed in this study was tested by applying to the satellite imagery covering Sunchon bay. This procedure can be an effective tool utilizing the scale characteristics of attribute information extracted from satellite imagery.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.107-115
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• 2003

This research investigated the 2001 spring drought on croplands in South Korea using satellite imagery. South Korea has suffered from spring droughts almost every year. Meteorological indices have been used for monitoring droughts, however they don't tell the local severity of drought. Therefore, this research aimed at detecting the local, spatial pattern of drought severity at a cropland level. This research analyzed the agricultural drought using the wetness of remotely sensed pixels that affects the growth of early crops significantly in the spring. This research, specifically, analyzed the spatial distribution and severity of drought using the tasseled cap transformation and topographical factors. The wetness index from the tasseled cap transformation of Landsat 7 ETM/sub ＋/ imagery was very useful for detecting the 2001 spring drought susceptibility in agricultural croplands. Especially, the wetness values smaller than -0.2 were identified as the croplands that were suffering from serious water deficit. Using the water deficit pixels, drought severity was modeled finally.

• Proceedings of the KSRS Conference
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• v.2
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• pp.643-646
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• 2006

This paper addresses the approach to extract linear features from satellite imagery using an efficient segmentation method. The extraction of linear features from satellite images has been the main concern of many scientists. There is a need to develop a more capable and cost effective method for the Iranian map revision tasks. The conventional approaches for producing, maintaining, and updating GIS map are time consuming and costly process. Hence, this research is intended to investigate how to obtain linear features from SPOT satellite imagery. This was accomplished using a discontinuity-based segmentation technique that encompasses four stages: low level bottom-up, middle level bottom-up, edge thinning and accuracy assessment. The first step is geometric correction and noise removal using suitable operator. The second step includes choosing the appropriate edge detection method, finding its proper threshold and designing the built-up image. The next step is implementing edge thinning method using mathematical morphology technique. Lastly, the geometric accuracy assessment task for feature extraction as well as an assessment for the built-up result has been carried out. Overall, this approach has been applied successfully for linear feature extraction from SPOT image.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.623-632
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• 2012

In this paper, we analyzed the effect of wavelet decomposition levels in feature extraction for anomaly detection from hyperspectral imagery. After wavelet analysis, anomaly detection was experimentally performed using the RX detector algorithm to analyze the detecting capabilities. From the experiment for anomaly detection using CASI imagery, the characteristics of extracted features and the changes of their patterns showed that radiance curves were simplified as wavelet transform progresses and H bands did not show significant differences between target anomaly and background in the previous levels. The results of anomaly detection and their ROC curves showed the best performance when using the appropriate sub-band decided from the visual interpretation of wavelet analysis which was L band at the decomposition level where the overall shape of profile was preserved. The results of this study would be used as fundamental information or guidelines when applying wavelet transform to feature extraction and selection from hyperspectral imagery. However, further researches for various anomaly targets and the quantitative selection of optimal decomposition levels are needed for generalization.