• Proceedings of the Korean Information Science Society Conference
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• 2010.06d
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• pp.360-365
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• 2010

In this paper, we present a distributed algorithm for detecting dynamic event regions in wireless sensor network with the consideration on energy saving. Our model is that the sensing field is monitored by a large number of randomly distributed sensors with low-power battery and limited functionality, and that the event region is dynamic with motion or changing the shape. At any time that the event happens, we need some sensors awake to detect it and to wake up its k-hop neighbors to detect further events. Scheduling for the network to save the total power-cost or to maximize the monitoring time has been studied extensively. Our scheme is that some predetermined sensors, called critical sensors are awake all the time and when the event is detected by a critical sensor the sensor broadcasts to the neighbors to check their sensing area. Then the neighbors check their area and decide whether they wake up or remain in sleeping mode with certain criteria. Our algorithm uses only 2 bit of information in communication between sensors, thus the total communication cost is low, and the speed of detecting all event region is high. We adapt two kinds of measure for the wake-up decision. With suitable threshold values, our algorithm can be applied for many applications and for the trade-off between energy saving and the efficiency of event detection.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.295-309
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• 2009

In this study, an iterative maximum a posteriori (MAP) approach using a Bayesian model of Markovrandom field (MRF) was proposed for despeckling images that contains speckle. Image process is assumed to combine the random fields associated with the observed intensity process and the image texture process respectively. The objective measure for determining the optimal restoration of this "double compound stochastic" image process is based on Bayes' theorem, and the MAP estimation employs the Point-Jacobian iteration to obtain the optimal solution. In the proposed algorithm, MRF is used to quantify the spatial interaction probabilistically, that is, to provide a type of prior information on the image texture and the neighbor window of any size is defined for contextual information on a local region. However, the window of a certain size would result in using wrong information for the estimation from adjacent regions with different characteristics at the pixels close to or on boundary. To overcome this problem, the new method is designed to use less information from more distant neighbors as the pixel is closer to boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The proximity to boundary is estimated using a non-uniformity measurement based on standard deviation of local region. The new scheme has been extensively evaluated using simulation data, and the experimental results show a considerable improvement in despeckling the images that contain speckle.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.139-152
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• 2004

This paper proposes to improve the results of image classification with spatial region growing segmentation by using an MRF-based classifier. The proposed approach is to re-classify the pixels in the boundary area, which have high probability of having classification error. The MRF-based classifier performs iteratively classification using the class parameters estimated from the region growing segmentation scheme. The proposed method has been evaluated using simulated data, and the experiment shows that it improve the classification results. But, conventional MRF-based techniques may yield incorrect results of classification for remotely-sensed images acquired over the ground area where has complicated types of land-use. A multistage MRF-based iterative class-modification in boundary is proposed to alleviate difficulty in classifying intricate land-cover. It has applied to remotely-sensed images collected on the Korean peninsula. The results show that the multistage scheme can produce a spatially smooth class-map with a more distinctive configuration of the classes and also preserve detailed features in the map.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.601-612
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• 2006

This study proposes RAG (Region Adjancency Graph)-based image segmentation for large imagery in remote sensing. The proposed algorithm uses CN-chain linking for computational efficiency and multi-window operation of sliding structure for memory efficiency. Region-merging due to RAG is a process to find an edge of the best merge and update the graph according to the merge. The CN-chain linking constructs a chain of the closest neighbors and finds the edge for merging two adjacent regions. It makes the computation time increase as much as an exact multiple in the increasement of image size. An RNV (Regional Neighbor Vector) is used to update the RAG according to the change in image configuration due to merging at each step. The analysis of large images requires an enormous amount of computational memory. The proposed sliding multi-window operation with horizontal structure considerably the memory capacity required for the analysis and then make it possible to apply the RAG-based segmentation for very large images. In this study, the proposed algorithm has been extensively evaluated using simulated images and the results have shown its potentiality for the application of remotely-sensed imagery.

• Proceedings of the KSRS Conference
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• v.1
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• pp.455-458
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• 2006

Stretching on the coastline of 70 km, the Tam Giang - Cau Hai Lagoon plays a very important role for the coastal ecology and socio-economic development of Hue region where was Vietnam's Ancient Kingdom Capital and recognized as a World's Cultural Heritage. Recently, coastal hazard in the lagoon have occurred seriously such as inlet movement and fill up, coastal erosion, flood and inundation, etc. These hazards have impacted on lagoon environment, resources, ecosystems, socio-economic and sustainable development of this coastal area. This paper present a case study using remote sensing data in combination with ground survey for monitoring the coastal hazards in Tam Giang - Cau Hai lagoon in recent decades. Analysis results find that during its natural evolution, the lagoon has been being in three situations of only one, two and three inlets. When inlets opened or displaced, coastal erosion have occurred seriously toward new balance condition. Flood and inundation occurs every rainy season in lowland plain around lagoon. The historical flood happened in early of November 1999 with six days long, created very terrible damages for Thua Thien Hue province. Remote sensing data with capability of regular update, large area coverage is effective provide real-time and continuous information for coastal hazards monitoring.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3432-3448
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• 2015

Interference can cause serious problems in our daily life. Traditional ways in localizing a target can't work well when it comes to the source of interference for it may take an uncooperative or even resistant attitude towards localization. To tackle this issue, we take the BPSN (Binary Proximity Sensor Networks) and consider a passive way in this paper. No cooperation is needed and it is based on simple sensor node suitable for large-scale deployment. By dividing the sensing field into different patches, when enough patches are formed, good localization accuracy can be achieved with high resolution. Then we analyze the relationship between sensing radius and localization error, we find that in a finite region where edge effect can't be ignored, the trend between sensing radius and localization error is not always consistent. Through theoretical analysis and simulation, we explore to determine the best sensing radius to achieve high localization accuracy.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1430-1432
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• 2003

Agricultural areas in the north region of Bangladesh suffer from water shortages during the dry season as well as dry spells in the monsoon period. The Teesta Barrage was constructed in 1990 to provide supplemental irrigation water during the monsoon period. After completion of the project high yielding variety of crops were introduced more in the project area. Due to this reason unforeseen needs of irrigation water during the dry season has emerged. This study reviews the current irrigation status and related constraints to a full development of the project and provides suggestions for future improvement of the project. Also suggested is to apply remote sensing technique for the management of the system as a whole. Use of remote sensing technique for the management of irrigation water resources is a new approach in Bangladesh. Application of such a powerful tool will provide better management options for large-scale irrigation projects in the country.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.421-434
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• 2012

Colored Dissolved Organic Matter (CDOM) is one of the important components of optical properties of seawater to determine ecosystem dynamics in a given marine area. The optical characteristics of CDOM may depend on the various ecosystem and environmental variables in the sea and those variables may vary region to region. Therefore, the retrieval algorithm for determining light absorption coefficient of CDOM ($a_{CDOM}$) using satellite remote sensing reflectance ($R_{rs}$) developed from other region may not be directly applicable to the other region, and it must be validated using an in-situ ground-truth observation. We have tested 6 known CDOM algorithms (three Semi-analytical and three Empirical CDOM algorithms) developed from other regions of the world ocean with laboratory determined in-situ values for the East Sea using field data collected during seven oceanographic cruises in the period of 2009~2011. Our field measurements extended from the coastal waters to the open oceanic type CASE-1 Waters. Our study showed that Quasi-Analytical Algorithm (QAA_v5) derived $a_{CDOM}$(412) appears to match in-situ $a_{CDOM}$(412) values statistically. Semi-analytical algorithms appeared to underestimate and empirical ones overestimated $a_{CDOM}$ in the East Sea. $a_{CDOM}$(412) value was found to be relatively high in the relatively high satellite derived-chlorophyll-a area. $a_{CDOM}$(412) value appears to be influenced by the amount of chlorophyll-a in seawater. The outcome of this work may be referenced to develop $a_{CDOM}$ algorithm for the new Korean Geostationary Ocean Color Imager (GOCI).

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2753-2756
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• 2007

In the present study, we reduce the drag and lift fluctuations of the sphere by providing a linear proportional control. For this purpose, we measure the radial velocity along the centerline in the wake and provide blowing and suction at a part of sphere surface based on the measured velocity. Zero-net mass flow rate is satisfied during the control. This control is applied to the flow over a sphere at Re=300 and 425. We vary the sensing location at $0.8d{\leq}X_s{\leq}1.3d$ and find that the most effective sensing region coincides with the location at which minimum correlation between the lift and sensing-velocity directions occurs. As a result, the lift and drag fluctuations are significantly reduced.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.137-141
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• 2018

Detecting the ego-lane of a vehicle (the lane on which the vehicle is currently running) is one of the basic techniques for a smart car. Vision sensing is a widely-used method for the ego-lane detection. Existing studies usually find road lane lines by detecting edge pixels in the image from a vehicle camera, and then connecting the edge pixels using Hough Transform. However, this approach takes rather long processing time, and too many straight lines are often detected resulting in false detections in various road conditions. In this paper, we find the lane lines by scanning only a limited number of horizontal lines within a small image region of interest. The horizontal image line scan replaces the edge detection process of existing methods. Automatic thresholding and spatiotemporal filtering procedures are also proposed in order to make our method reliable. In the experiments using real road images of different conditions, the proposed method resulted in high success rate.