• Title/Summary/Keyword: Minimum Distance Classification

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A study on the classifying vehicles for traffic flow analysis using LiDAR DATA

  • Heo J.Y.;Choi J.W.;Kim Y.I.;Yu K.Y.
    • Proceedings of the KSRS Conference
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    • 2004.10a
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    • pp.633-636
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    • 2004
  • Airborne laser scanning thechnology has been studied in many applications, DSM(Digital Surface Model) development, building extraction, 3D virtual city modeling. In this paper, we will evaluate the possibility of airborne laser scanning technology for transportation application, especially for recognizing moving vehicles on road. First, we initially segment the region of roads from all LiDAR DATA using the GIS map and intensity image. Secondly, the segmented region is divided into the roads and vehicles using the height threshold value of local based window. Finally, the vehicles will be classified into the several types of vehicles by MDC(Minimum Distance Classification) method using the vehicle's geometry information, height, length, width, etc

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Classification of Underwater Transient Signals Using MFCC Feature Vector (MFCC 특징 벡터를 이용한 수중 천이 신호 식별)

  • Lim, Tae-Gyun;Hwang, Chan-Sik;Lee, Hyeong-Uk;Bae, Keun-Sung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.8C
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    • pp.675-680
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    • 2007
  • This paper presents a new method for classification of underwater transient signals, which employs frame-based decision with Mel Frequency Cepstral Coefficients(MFCC). The MFCC feature vector is extracted frame-by-frame basis for an input signal that is detected as a transient signal, and Euclidean distances are calculated between this and all MFCC feature. vectors in the reference database. Then each frame of the detected input signal is mapped to the class having minimum Euclidean distance in the reference database. Finally the input signal is classified as the class that has maximum mapping rate in the reference database. Experimental results demonstrate that the proposed method is very promising for classification of underwater transient signals.

The Classifications using by the Merged Imagery from SPOT and LANDSAT

  • Kang, In-Joon;Choi, Hyun;Kim, Hong-Tae;Lee, Jun-Seok;Choi, Chul-Ung
    • Proceedings of the KSRS Conference
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    • 1999.11a
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    • pp.262-266
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    • 1999
  • Several commercial companies that plan to provide improved panchromatic and/or multi-spectral remote sensor data in the near future are suggesting that merge datasets will be of significant value. This study evaluated the utility of one major merging process-process components analysis and its inverse. The 6 bands of 30$\times$30m Landsat TM data and the 10$\times$l0m SPOT panchromatic data were used to create a new 10$\times$10m merged data file. For the image classification, 6 bands that is 1st, 2nd, 3rd, 4th, 5th and 7th band may be used in conjunction with supervised classification algorithms except band 6. One of the 7 bands is Band 6 that records thermal IR energy and is rarely used because of its coarse spatial resolution (120m) except being employed in thermal mapping. Because SPOT panchromatic has high resolution it makes 10$\times$10m SPOT panchromatic data be used to classify for the detailed classification. SPOT as the Landsat has acquired hundreds of thousands of images in digital format that are commercially available and are used by scientists in different fields. After the merged, the classifications used supervised classification and neural network. The method of the supervised classification is what used parallelepiped and/or minimum distance and MLC(Maximum Likelihood Classification) The back-propagation in the multi-layer perception is one of the neural network. The used method in this paper is MLC(Maximum Likelihood Classification) of the supervised classification and the back-propagation of the neural network. Later in this research SPOT systems and images are compared with these classification. A comparative analysis of the classifications from the TM and merged SPOT/TM datasets will be resulted in some conclusions.

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Performance of an ML Modulation Classification of QAM Signals with Single-Sample Observation (단일표본관측을 이용한 직교진폭변조 신호의 치운 변조분류 성능)

  • Kang Seog Geun
    • The KIPS Transactions:PartC
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    • v.12C no.1 s.97
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    • pp.63-68
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    • 2005
  • In this paper, performance of a maximum-likelihood modulation classification for quadrature amplitude modulation (QAM) is studied. Unlike previous works, the relative classification performance with respect to the available modulations and performance limit with single-sample observation are presented. For those purposes, all constellations are set to have the same minimum Euclidean distance between symbols so that a smaller constellation is a subset of the larger ones. And only one sample of received waveform is used for multiple hypothesis test. As a result, classification performance is improved with increase in signal-to-noise ratio in all the experiments. Especially, when the true modulation format used in the transmitter is 4 QAM, almost perfect classification can be achieved without any additional information or observation samples. Though the possibility of false classification due to the symbols shared by subset constellations always exists, correct classification ratio of $80{\%}$ can be obtained with the single-sample observation when the true modulation formats are 16 and 64 QAM.

Object-based Image Classification by Integrating Multiple Classes in Hue Channel Images (Hue 채널 영상의 다중 클래스 결합을 이용한 객체 기반 영상 분류)

  • Ye, Chul-Soo
    • Korean Journal of Remote Sensing
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    • v.37 no.6_3
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    • pp.2011-2025
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    • 2021
  • In high-resolution satellite image classification, when the color values of pixels belonging to one class are different, such as buildings with various colors, it is difficult to determine the color information representing the class. In this paper, to solve the problem of determining the representative color information of a class, we propose a method to divide the color channel of HSV (Hue Saturation Value) and perform object-based classification. To this end, after transforming the input image of the RGB color space into the components of the HSV color space, the Hue component is divided into subchannels at regular intervals. The minimum distance-based image classification is performed for each hue subchannel, and the classification result is combined with the image segmentation result. As a result of applying the proposed method to KOMPSAT-3A imagery, the overall accuracy was 84.97% and the kappa coefficient was 77.56%, and the classification accuracy was improved by more than 10% compared to a commercial software.

A Study of Post-processing Methods of Clustering Algorithm and Classification of the Segmented Regions (클러스터링 알고리즘의 후처리 방안과 분할된 영역들의 분류에 대한 연구)

  • Oh, Jun-Taek;Kim, Bo-Ram;Kim, Wook-Hyun
    • The KIPS Transactions:PartB
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    • v.16B no.1
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    • pp.7-16
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    • 2009
  • Some clustering algorithms have a problem that an image is over-segmented since both the spatial information between the segmented regions is not considered and the number of the clusters is defined in advance. Therefore, they are difficult to be applied to the applicable fields. This paper proposes the new post-processing methods, a reclassification of the inhomogeneous clusters and a region merging using Baysian algorithm, that improve the segmentation results of the clustering algorithms. The inhomogeneous cluster is firstly selected based on variance and between-class distance and it is then reclassified into the other clusters in the reclassification step. This reclassification is repeated until the optimal number determined by the minimum average within-class distance. And the similar regions are merged using Baysian algorithm based on Kullbeck-Leibler distance between the adjacent regions. So we can effectively solve the over-segmentation problem and the result can be applied to the applicable fields. Finally, we design a classification system for the segmented regions to validate the proposed method. The segmented regions are classified by SVM(Support Vector Machine) using the principal colors and the texture information of the segmented regions. In experiment, the proposed method showed the validity for various real-images and was effectively applied to the designed classification system.

The Evaluation of on Land Cover Classification using Hyperspectral Imagery (초분광 영상을 이용한 토지피복 분류 평가)

  • Lee, Geun-Sang;Lee, Kang-Cheol;Go, Sin-Young;Choi, Yun-Woong;Cho, Gi-Sung
    • Journal of Cadastre & Land InformatiX
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    • v.44 no.2
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    • pp.103-112
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    • 2014
  • The objective of this study is to suggest the possibility on land cover classification using hyperspectal imagery on area which includes lands and waters. After atmospheric correction as a preprocessing work was conducted on hyperspectral imagery acquired by airborne hyperspectral sensor CASI-1500, the effect of atmospheric correction to a few land cover class in before and after atmospheric correction was compared and analyzed. As the result of accuracy of land cover classification by highspectral imagery using reference data as airphoto and digital topographic map, maximum likelihood method represented overall accuracy as 67.0% and minimum distance method showed overall accuracy as 52.4%. Also product accuracy of land cover classification on road, dry field and green house, but that on river, forest, grassland showed low because the area of those was composed of complex object. Therefore, the study needs to select optimal band to classify specific object and to construct spectral library considering spectral characteristics of specific object.

A Study on the Preparation Method of Fruit Cropping Distribution Map using Satellite Images and GIS (위성영상과 GIS를 이용한 과수재배 분포도 작성 기법에 관한 연구)

  • Jo, Myung-Hee;Bu, Ki-Dong;Lee, Jung-Hyoup;Lee, Kwang-Jae
    • Journal of the Korean Association of Geographic Information Studies
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    • v.3 no.4
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    • pp.73-86
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    • 2000
  • This study focused on extracting an efficient method in the fruit cropping distribution mapping with various classification methods using multi-temporal satellite images and Geographic Information Systems(GIS). For this study, multi-temporal Landsat TM images, in observation data and existing fruit cropping area statistics were used to compare and analyze the properties of fruit cropping and seasonal distribution per classification method. As a result, this study concludes that Maximum Likelihood Method with earlier autumn satellite image was most efficient for the fruit cropping mapping using Landsat TM image. In addition, it was clarified that cropping area per administrative boundary was prepared and distribution pattern was identified efficiently using GIS spatial analysis.

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Sub-Pixel Analysis of Hyperspectral Image Using Linear Spectral Mixing Model and Convex Geometry Concept

  • Kim, Dae-Sung;Kim, Yong-Il;Lim, Young-Jae
    • Korean Journal of Geomatics
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    • v.4 no.1
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    • pp.1-8
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    • 2004
  • In the middle-resolution remote sensing, the Ground Sampled Distance (GSD) that the detector senses and samples is generally larger than the actual size of the objects (or materials) of interest, and so several objects are embedded in a single pixel. In this case, as it is impossible to detect these objects by the conventional spatial-based image processing techniques, it has to be carried out at sub-pixel level through spectral properties. In this paper, we explain the sub-pixel analysis algorithm, also known as the Linear Spectral Mixing (LSM) model, which has been experimented using the Hyperion data. To find Endmembers used as the prior knowledge for LSM model, we applied the concept of the convex geometry on the two-dimensional scatter plot. The Atmospheric Correction and Minimum Noise Fraction techniques are presented for the pre-processing of Hyperion data. As LSM model is the simplest approach in sub-pixel analysis, the results of our experiment is not good. But we intend to say that the sub-pixel analysis shows much more information in comparison with the image classification.

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Classification of the Somatotypes for the Construction of Young Women's Clothing (Part 1) (청년기 여성의 의복설계를 위한 체형분류 (제1보))

  • 권숙희;김혜경
    • Journal of the Korean Society of Clothing and Textiles
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    • v.20 no.2
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    • pp.282-297
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    • 1996
  • The effective construction for ready-made clothes is one of the central concerns of both consumers and manufactuers in today's apparel industry. In order to reduce the burden of stocks and increase clothing fitness, systematic information on typical body sizes and somatotypes is essential. The purpose of this study i-: to provide basic data on young women's somatotypes for form designers and pattern makers. The subjects of the survey were 310 women of 18 to 26 years old. The study collected 84 anthropometric data for each Person. The data was analyzed by using of the multivariate method. The factor analysis was utilized in regard to the 65 items obtained from anthropometric measurement respectively. The principal component analysis was applied to the data with orthogonal rotation after extraction. The factor scores used in the factor analysis became the basis of determining the value of each variable of the cluster analysis. The cluster analysis was applied for identifying typical somatotypes. Ward's minimum variance method was applied for the purpose of extracting distance metrix by the standardized Euclidean distance. The element forming each cluster can be subdivided into several sets by crosstabulation which is obtained by the fastclus of the SAS. This research has demonstrated 3 distinctive types of silhouette contour of the trunk. Incidentally it also identified 4 of the lower body from the waistline to thigh contour respectively. The discriminant analysis showed that the most significant discriminant factor of the trunk classification were side neck point -1 scapular -1 waistiline length and waist girth. In Korea, the average somatotype of female college students tends to be tall, slim and straight. Reviewing the relationship between the classifications of three parts of body, they are related to each other to some extent but their distribution are not constant. Therefore, in view of clothing construction, a proper separation of the body surface is a necessity.

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