• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.421-433
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• 2011

The importance of acquisition of road information has recently been increased with a rapid growth of spatial-related services such as urban information system and location based service. This paper proposes an automatic road extraction method using object-based approach which was issued alternative of pixel-based method recently. Firstly, the spatial objects were created by MSRS(Modified Seeded Region Growing) method, and then the key road objects were extracted by using properties of objects such as their shape feature information and adjacency. The omitted road objects were also traced considering spatial correlation between extracted road and their neighboring objects. In the end, the final road region was extracted by connecting discontinuous road sections and improving road surfaces through their geometric properties. To assess the proposed method, quantitative analysis was carried out. From the experiments, the proposed method generally showed high road detection accuracy and had a great potential for the road extraction from high resolution satellite images.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.29-39
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• 2020

Forest fire is a critical disaster that causes massive destruction of forest ecosystem and economic loss. Hence, accurate estimation of the burned area is important for evaluation of the degree of damage and for preparing baseline data for recovery. Since most of the area size damaged by wildfires in Korea is less than 1 ha, it is necessary to use satellite or drone images with a resolution of less than 10m for detecting the damage area. This paper aims to detect wildfire-damaged area from a Kompsat-3 image using the indices such as NDVI (normalized difference vegetation index) and FBI (fire burn index) and to examine the classification characteristics according to the methods such as Otsu thresholding and ISODATA(iterative self-organizing data analysis technique). To mitigate the salt-and-pepper phenomenon of the pixel-based classification, a gaussian filter was applied to the images of NDVI and FBI. Otsu thresholding and ISODATA could distinguish the burned forest from normal forest appropriately, and the salt-and-pepper phenomenon at the boundaries of burned forest was reduced by the gaussian filter. The result from ISODATA with gaussian filter using NDVI was closest to the official record of damage area (56.9 ha) published by the Korea Forest Service. Unlike Otsu thresholding for binary classification,since the ISODATA categorizes the images into multiple classes such as(1)severely burned area, (2) moderately burned area, (3) mixture of burned and unburned areas, and (4) unburned area, the characteristics of the boundaries consisting of burned and normal forests can be better expressed. It is expected that our approach can be utilized for the high-resolution images obtained from other satellites and drones.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.391-402
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• 2016

Among the various remote sensing sensors compared to the electro-optical sensors, SAR (Synthetic Aperture Radar) is very suitable for assessing damaged areas induced by disaster events owing to its all-weather day and night acquisition capability and sensitivity to geometric variables. The conventional CD (Change Detection) method that uses two-date data is typically used for mapping damage over extensive areas in a short time, but because data from only two dates are used, the information used in the conventional CD is limited. In this paper, we propose a novel CD method that is extended to use data consisting of two pre-disaster SAR data and one post-disaster SAR data. The proposed CD method detects changes by using a similarity weight image derived from the neighborhood information of a pixel in the data from the three dates. We conducted an experiment using three single polarization ALOS PALSAR (Advanced Land Observing Satellite/Phased Array Type L-Band) data collected over Miyagi, Japan which was seriously damaged by the 2011 east Japan tsunami. The results demonstrated that the mapping accuracy for damaged areas can be improved by about 26% with an increase of the g-mean compared to the conventional CD method. These improved results prove the performance of our proposed CD method and show that the proposed CD method is more suitable than the conventional CD method for detecting damaged areas induced by disaster.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.4
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• pp.443-452
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• 2008

To deduce 3D linear information which express shapes of buildings out of airphoto by fusion of airphoto and LiDAR data, this research went through 2 process. First, research made LiDAR data into projected data of 2D based on airphoto. For this, the virtual points were added to solve the visual problem of building boundary area which has poor information because the attribute in LiDAR data. Research construct irregular triangular nets from modified LiDAR data and judge visual triangular nets out of image. Through this, research can make reference to information of triangular nets in each image pixel. Second, 3D information was extracted from stereo images segments by combining extracted information of visible region and 2D irregular triangular nets. Matching way based on TIN for segments from stereo images was used. Matching condition based on TIN can improve about 20% of edge matching accuracy compared to existing quadrilateral condition of epipolar geometry.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.258-266
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• 2019

This study was conducted to apply natural river technologies to levees and examine the results. The new eco-friendly bio-polymer was applied, a combination of eco-friendly biopolymers and soil, to levee slope to enhance durability and eco-friendliness and to establish reinforcement measures against unstable levees due to overtopping. A semi-prototype levee of 1 m in height, 3 m in width, with a 1:2 slope and 5 m length, was constructed at the Andong River Experiment Center. The bio-soil mixed with the biopolymer and the soil at an appropriate ratio was treated with a 5 cm thickness on the surface of levee to perform the stability evaluation according to overtopping. Using the pixel-based analysis technique using the image analysis program, the breached area of levee slope was calculated over time. As a result, the time for complete decay occurs more than 12 times than that of ordinary soil levee. Therefore, when the new substance is applied to the surface of levee, the decay delay effect appears to be high.

• Smart Media Journal
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• v.9 no.2
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• pp.9-15
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• 2020

In this paper, we propose a network model composed of Multi path Encoder-Decoder branches that can recognize each instance from the image. The network has two branches, Dot branch and Segmentation branch for finding the center point of each instance and for recognizing area of the instance, respectively. In the experiment, the CVPPP dataset was studied to distinguish leaves from each other, and the center point detection branch(Dot branch) found the center points of each leaf, and the object segmentation branch(Segmentation branch) finally predicted the pixel area of each leaf corresponding to each center point. In the existing segmentation methods, there were problems of finding various sizes and positions of anchor boxes (N > 1k) for checking objects. Also, there were difficulties of estimating the number of undefined instances per image. In the proposed network, an effective method finding instances based on their center points is proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.6
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• pp.1526-1537
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• 1998

The split-screen function on the multipoint control unit(MCU) which is usually using processing method based on pixel domain has many problems for manipulating the video signal in real-time. Although the researches and the developements to cope ith such problems are processing, these have too complex architecture to implement and are limited to method for H.261 video signal. In this paper, we propose a new mechanism for split-screen that can actually apply to ISO/IEC MPEG video standard. The new method that is proposed in this paper do the processes in complete compression domain, thus it is suitable for the application of real-time multipoint multimedia communication service. By simple interpreting and manipulating the MPEG video element stream, the split-screen functional module can be implemented easily and the result of the procedures does not accompany image degradation at all. Finally, the complexity of implementation, the aspect for processing delay and the loss of image quality as compared to that resulting in the case of applying the previous split-screen method has been investigated. And it is confirmed that the proposed mechanism has a significant advantage as a split-screen method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12B
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• pp.1766-1774
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• 2010

This paper proposes a real-time algorithm of recognizing speed limit signs for intelligent vehicles. Contrary to previous works which use all the pixel values in the ROI (Region Of Interest) after preprocessing image at ROI and need a lot of operations, the proposed algorithm uses fewer DCT coefficients in the ROI as features of each image to reduce the number of operations. Choosing a portion of DCT coefficients which satisfy discriminant criteria for recognition, the proposed algorithm recognizes the speed limit signs using the information obtained in the selected features through LDA and MD. It selects one having the highest probability among the recognition results calculated by accumulating the classification results of consecutive individual frames. Experimental results show that the recognition rate for consecutive frames reaches to 100% with test images. When compared with the previous algorithm, the numbers of multiply and add operations are reduced by 58.6% and 38.3%, respectively.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.737-743
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• 2014

For single image super resolution (SR), interpolation based and example based algorithms are extensively used. The interpolation algorithms have the strength of theoretical simplicity. However, those algorithms are tending to produce high resolution images with jagged edges, because they are not able to use more priori information. Example based algorithms have been studied in the past few years. For example based SR, the nearest neighbor based algorithms are extensively considered. Among them, neighbor embedding (NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the sizes of local training sets are always too small. So, NE algorithm is weak in the performance of the visuality and quantitative measure by the poor generalization of nearest neighbor estimation. An improved NE algorithm with Support Vector Regression (SVR) was proposed to solve this problem. Given a low resolution image, the pixel values in its high resolution version are estimated by the improved NE. Comparing with bicubic and NE, the improvements of 1.25 dB and 2.33 dB are achieved in PSNR. Experimental results show that proposed method is quantitatively and visually more effective than prior works using bicubic interpolation and NE.