• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.361-364
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• 2008

It is well known that combining spatial and spectral information can improve land use classification from satellite imagery. High spatial resolution classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, extracting the spatial information is one of the most important steps in high resolution satellite image classification. In this paper, we propose a new spatial feature extraction method. The extracted features are integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a Support Vector Machines classifier. In order to evaluate the proposed feature extraction method, we applied our approach to KOMPSAT-2 data and compared the result with the other methods.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.204-206
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• 2003

In order to utilize remote sensed images effectively, a lot of image classification methods are suggested for many years. But, the accuracy of traditional methods based on pixel-based classification is not high in general. In this study, object oriented classification based on image segmentation is used to classify Landsat images. A necessary prerequisite for object oriented image classification is successful image segmentation. Object oriented image classification, which is based on fuzzy logic, allows the integration of a broad spectrum of different object features, such as spectral values , shape and texture. Landsat images are divided into urban, agriculture, forest, grassland, wetland, barren and water in sochon-gun, Chungcheongnam-do using object oriented classification algorithms in this paper. Preliminary results will help to perform an automatic image classification in the future.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.213-217
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• 1998

This study overviews existing evaluation methods of classification accuracy using confusion matrix proposed by Cohen in 1960's, and proposes ISDd(Index of Spatial Distribution by distance) and ISDs(Index of Spatial Distribution by scatteredness) for the evaluation of spatial distribution of satellite image classification errors, which has not been tried yet. Index of spatial distribution offers the basis of decision on adoption/rejection of classification results at sub-image level by evaluation of distribution, such as status of local aggregation of misclassified pixels. So, users can understand the spatial distribution of misclassified pixels and, can have the basis of judgement of suitability and reliability of classification results.

• Korean Institute of Interior Design Journal
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• v.24 no.1
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• pp.207-215
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• 2015

It seems obvious that the spatial information of existing architectural heritage will be re-structured utilizing BIM technology. In the future to be able to implement such task, a new system of classification of spatial information, which fit to the structural nature of architectural heritage is necessary. This paper intend to suggest the conceptual model that can be the base of establishing new classification system for architectural heritage. For this study we reviewed researches related to classification system of architectural heritage (CS-AH) and BIM based architectural heritage (BIM-AH), first. As a result, we found that CS-AH is focused on building elevation and type, and BIM-AH is biased on the Library and Parametric Modeling. Second, we figured out a relationship between the CS-AH and BIM-AH. From this analysis, we found that BIM-AH is biased on Library and Parametric since the building elevation and type was focused on CS-AH. This review suggests a potential of the 3D CS-AH to expand the range of research for BIM-AH. At last, we suggest the three concept of classification are: 1)horizontality-accumulation relationship, 2)structure-infill relationship, 3)segment-member relationship. These three concept, together as one system of classification, could provide useful framework of new classification system of spatial information for architectural heritage.

• Journal of Korean Society for Geospatial Information Science
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• v.9 no.2 s.18
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• pp.37-46
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• 2001

Spectral pattern recognition techniques are most used in classification of remotely sensed data. Yet, in any real image, adjacent pixels are related, because imaging sensors acquire significant portions of energy from adjacent pixels. And, with the continued improvement in the spatial resolution of remote sensing systems, another spatial pattern recognition approach is must considered. In this study, we aim to show the potentiality of spatial classification methods through comparing the accuracies of spectral classification methods and those of spectral classification methods. By the comparisons between the two methods, classification accuracies of 6 different spatial classification methods are higher than that of spectral classification method by 2-6% or so. Additionally, we can show it statistically through the classification experiments with different band combinations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.807-824
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• 2016

In this paper, a method of mood classification based on user brainwaves is proposed for real-time application in commercial services. Unlike conventional mood analyzing systems, the proposed method focuses on classifying real-time user moods by analyzing the user's brainwaves. Applying brainwave-related research in commercial services requires two elements - robust performance and comfortable fit of. This paper proposes a filter based on Regularized Common Spatial Patterns (RCSP) and presents its use in the implementation of mood classification for a music service via a wireless consumer electroencephalography (EEG) device that has only 14 pins. Despite the use of fewer pins, the proposed system demonstrates approximately 10% point higher accuracy in mood classification, using the same dataset, compared to one of the best EEG-based mood-classification systems using a skullcap with 32 pins (EU FP7 PetaMedia project). This paper confirms the commercial viability of brainwave-based mood-classification technology. To analyze the improvements of the system, the changes of feature variations after applying RCSP filters and performance variations between users are also investigated. Furthermore, as a prototype service, this paper introduces a mood-based music list management system called MyMusicShuffler based on the proposed mood-classification method.

• Journal of the Korean Institute of Educational Facilities
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• v.23 no.4
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• pp.3-10
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• 2016

This paper presents the investigation results on the classification of the university campuses. For the classification, we selected the spatial index as the evaluation indicator since the environmental factors and maintenance methods vary from university campus to university campus. For the study, we used eight spatial indices of the 30 national universities. This paper provides the spatial characteristics of different campus types, presents campus classification analysis as a future research approach to campus maintenance, and provides the data for the future study of comparison among universities. The results are as follows. 1) The classification investigation categorized the university campuses into three groups. Type 1 is a large-scale type, located near downtown. Type 2 is a medium-scale type, located at a remote site from downtown. Type 3 is a small-scale type, which is located comparatively near downtown. 2) Type 1 is a large-scale mixed area type, and 13 universities belong to this group. Type 2 is a medium-scale suburban area type, and six universities are in this group. Finally, Type 3 is a small-scale downtown area type, and 11 universities belong to this group.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.1
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• pp.1-10
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• 2017

An UAV (Unmanned Aerial Vehicle) is a flight system that is designed to conduct missions without a pilot. Compared to traditional airborne-based photogrammetry, UAV-based photogrammetry is inexpensive and can obtain high-spatial resolution data quickly. In this study, we aimed to classify the land cover using high-spatial resolution images obtained using a UAV. An RGB camera was used to obtain high-spatial resolution orthoimage. For accurate classification, multispectral image about same areas were obtained using a multispectral sensor. A DSM (Digital Surface Model) and a modified NDVI (Normalized Difference Vegetation Index) were generated using images obtained using the RGB camera and multispectral sensor. Pixel-based classification was performed for twelve classes by using the RF (Random Forest) method. The classification accuracy was evaluated based on the error matrix, and it was confirmed that the proposed method effectively classified the area compared to supervised classification using only the RGB image.

• ETRI Journal
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• v.43 no.2
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• pp.272-287
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• 2021

This article presents a multi-dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification-based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime-prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real-world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1273-1283
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• 2019

This paper proposes an advanced crop classification model that combines a procedure for weighted combination of spatial features extracted from multi-scale input images with a conventional convolutional neural network (CNN) structure. The proposed model first extracts spatial features from patches with different sizes in convolution layers, and then assigns different weights to the extracted spatial features by considering feature-specific importance using squeeze-and-excitation block sets. The novelty of the model lies in its ability to extract spatial features useful for classification and account for their relative importance. A case study of crop classification with multi-temporal Landsat-8 OLI images in Illinois, USA was carried out to evaluate the classification performance of the proposed model. The impact of patch sizes on crop classification was first assessed in a single-patch model to find useful patch sizes. The classification performance of the proposed model was then compared with those of conventional two CNN models including the single-patch model and a multi-patch model without considering feature-specific weights. From the results of comparison experiments, the proposed model could alleviate misclassification patterns by considering the spatial characteristics of different crops in the study area, achieving the best classification accuracy compared to the other models. Based on the case study results, the proposed model, which can account for the relative importance of spatial features, would be effectively applied to classification of objects with different spatial characteristics, as well as crops.