• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.959-971
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• 2019

CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.395-408
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• 2015

High-resolution satellite image mosaics are becoming increasingly important in the field of remote sensing image analysis as an essential image processing to create a large image constructed from several smaller images. In this paper, we present an automatic seamline extraction technique and the procedure to generate a mosaic image by this technique. For more effective seamline extraction in the overlap region of adjacent images, an NDVI-based seamline extraction technique is developed, which takes advantage of the computational time and memory. The Normalized Difference Vegetation Index(NDVI) is an index of plant "greeness" or photosynthetic activity that is employed to extract the initial seamline. The NDVI can divide into manmade region and natural region. The cost image is obtained by the canny edge detector and the buffering technique is used to extract the ranging cost image. The seamline is extracted by applying the Dijkstra algorithm to a cost image generated through the labeling process of the extracted edge information. Histogram matching is also conducted to alleviate radiometric distortion between adjacent images acquired at different time. In the experimental results using the KOMPSAT-2/3 satellite imagery, it is confirmed that the proposed method greatly reduces the visual discontinuity caused by geometric difference of adjacent images and the computation time.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.1
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• pp.153-176
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• 2010

The purpose of this study is to rethink the importance of manipulative materials and to extract of manipulative materials program and its application methods. Activity, construction, and operation is stressed in the elementary mathematics. For this, various technological tools and manipulative materials is emphasized in mathematics teaching-learning methods. Applications of manipulative materials in the elementary mathematics is gradually increased together with curriculum revisions and textbook developments. As a result, tangram, geo-board etc., many tools ate introduces to school mathematics. This study is executed in this contexts. To achieve this, We introduce Australian 'Maths With Attitude' program. This program is composed of the primary level and secondary level. Each level consists of four domains - Number & Computation, Space & Logic, Chance & Measurement, Pattern & Algebra -, and each domains is made up of 20 tasks(i.e. manipulative materials) and programs. This study takes the focus to 5-6 grades programs in the mid of the primary level. First, We introduce 'Monkeys & Bananas'(Number & Computation) and 'Triangles & Colours' (Pattern & Algebra) tasks, and investigate the examples of lessons using these tasks. Second, We think the probability of these tasks' application and draw examples in the elementary mathematic textbooks. Through this works, We respect teaching-learning methods is rich and various in the elementary mathematics lessons.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2784-2790
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• 2015

The column-oriented database storage is a very advanced model for large-volume data transactions because of its superior I/O performance. Traditional data storages exploit row-oriented storage where the attributes of a record are placed contiguously in hard disk for fast write operations. However, for search-mostly data warehouse systems, column-oriented storage has become a more proper model because of its superior read performance. Recently, solid state drive using flash memory is largely recognized as the preferred storage media for high-speed data analysis systems. In this research, we propose a new transaction recovery scheme for a column-oriented database environment which is based on a flash media file system. We improved traditional shadow paging schemes by reusing old data pages which are supposed to be invalidated in the course of writing a new data page in the flash file system environment. In order to reuse these data pages, we exploit reused shadow list structure in our column-oriented shadow recovery(CoSR) scheme. CoSR scheme minimizes the additional storage overhead for keeping shadow pages and minimizes the I/O performance degradation caused by column data compression of traditional recovery schemes. Based on the results of the performance evaluation, we conclude that CoSR outperforms the traditional schemes by 17%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2149-2156
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• 2011

In this paper, design of a new 3-dimensional (3-D) 16-ary signal constellation with constant envelope is presented and analyzed. Unlike the conventional 16-ary constellations, all signal points of the new constellation are uniformly located on the surface of a sphere so that they have a unique amplitude level and a symmetrical structure. When average power of the constellations is normalized, the presented 16-ary constellation has around 11.4% increased minimum Euclidean distance (MED) as compared to the conventional ones that have non-constant envelope. As a result, a digital communication system which exploits the presented constellation has 1.2dB improved symbol error rate (SER). While signal points of the conventional constant-envelope constellation are not distributed uniformly on the surface of a sphere, those of the proposed constellation has a completely symmetric distribution. In addition, the new signal constellation has much lower computational complexity for practical implementation than the conventional one. Hence, the proposed 3-D 16-ary signal constellation is appropriate for the application to a communication system which strongly requires a constant-envelope characteristic.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.87-98
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• 2011

Detecting a moving object from videos and tracking it are basic and necessary preprocessing steps in many video systems like object recognition, context aware, and intelligent visual surveillance. In this paper, we propose a method that is able to detect a moving object quickly and accurately in a condition that background and light change in a real time. Furthermore, our system detects strongly an object in a condition that the target object is covered with other objects. For effective detection, effective Eigen-space and FCM are combined and employed, and a CONDENSATION algorithm is used to trace a detected object strongly. First, training data collected from a background image are linear-transformed using Principal Component Analysis (PCA). Second, an Eigen-background is organized from selected principal components having excellent discrimination ability on an object and a background. Next, an object is detected with FCM that uses a convolution result of the Eigen-vector of previous steps and the input image. Finally, an object is tracked by using coordinates of an detected object as an input value of condensation algorithm. Images including various moving objects in a same time are collected and used as training data to realize our system that is able to be adapted to change of light and background in a fixed camera. The result of test shows that the proposed method detects an object strongly in a condition having a change of light and a background, and partial movement of an object.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.141-148
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• 2011

Most of the previous works on scene change detection algorithm focus on the detection of abrupt rather than gradual changes. In general, gradual scene change detection algorithms require heavy computation. Some of those approaches don't consider the error factors such as flashlights, camera or object movements, and special effects. Many scenes change detection algorithms based on the histogram show better performances than other approaches, but they have computation load problem. In this paper, we proposed a scene change detection algorithm with fast and accurate performance using the vertical and horizontal blocked slice images and their macro block informations. We apply graph cut partitioning algorithm for clustering and partitioning of video sequence using generated spatio-temporal histogram. When making spatio-temporal histogram, we only use the central block on vertical and horizontal direction for performance improvement. To detect camera and object movement as well as various special effects accurately, we utilize the motion vector and type information of the macro block.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.11
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• pp.24-32
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• 2002

In this paper we implemented various image processing filtering using the format converter. This design method is based on realized the large processor-per-pixel array by integrated circuit technology. These two types of integrated structure are can be classify associative parallel processor and parallel process DRAM(or SRAM) cell. Layout pitch of one-bit-wide logic is identical memory cell pitch to array high density PEs in integrate structure. This format converter design has control path implementation efficiently, and can be utilize the high technology without complicated controller hardware. Sequence of array instruction are generated by host computer before process start, and instructions are saved on unit controller. Host computer is executed the pixel-parallel operation starting at saved instructions after processing start. As a result, we obtained three result that 1)simple smoothing suppresses higher spatial frequencies, reducing noise but also blurring edges, 2) a smoothing and segmentation process reduces noise while preserving sharp edges, and 3) median filtering, like smoothing and segmentation, may be applied to reduce image noise. Median filtering eliminates spikes while maintaining sharp edges and preserving monotonic variations in pixel values.

• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.729-741
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• 2012

Among the methods of precipitation data acquisition, a rain gauge station has a distinctive advantage of direct measurement of rainfall itself, but multiple stations should be installed in order to obtain areal precipitation data required for hydrological analysis. On the other hand, a rainfall radar may provide areal distribution of rainfall in real time though it is an indirect measurement of radar echoes on rain drops. Rainfall radars have been shown useful especially for forecasting short-term localized torrential storms that may cause catastrophic flash floods. CAPPI (Constant Altitude Plan Position Indicator), which is one of the several types of radar rainfall image data, has been provided on the Internet in real time by Korea Meteorological Administration (KMA). It is one of the most widely available rainfall data in Korea with fairly high level of confidence as it is produced with bias adjustment and quality control procedures by KMA. The objective of this study is to develop an improved way to extract quantitative rainfall data applicable to even very small watersheds from CAPPI using CIVCOM, which is a new image processing method based on a vector-based scheme proposed in this study rather than raster-based schemes proposed by other researchers. This study shows usefulness of CIVCOM through comparison of rainfall data produced by image processing methods including traditional raster-based schemes and a newly proposed vector-based one.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.201-214
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• 2020

This study aimed to early detect damaged trees by pine wilt disease using the vegetation indices of UAV images. The location data of 193 pine wilt disease trees were constructed through field surveys and vegetation index analyses of NDVI, GNDVI, NDRE and SAVI were performed using multi-spectral UAV images at the same time. K-Means algorithm was adopted to classify damaged trees and confusion matrix was used to compare and analyze the classification accuracy. The results of the study are summarized as follows. First, the overall accuracy of the classification was analyzed in order of NDVI (88.04%, Kappa coefficient 0.76) > GNDVI (86.01%, Kappa coefficient 0.72) > NDRE (77.35%, Kappa coefficient 0.55) > SAVI (76.84%, Kappa coefficient 0.54) and showed the highest accuracy of NDVI. Second, K-Means unsupervised classification method using NDVI or GNDVI is possible to some extent to find out the damaged trees. In particular, this technique is to help early detection of damaged trees due to its intensive operation, low user intervention and relatively simple analysis process. In the future, it is expected that the utilization of time series images or the application of deep learning techniques will increase the accuracy of classification.