• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.59-64
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• 2001

The objectives of the thesis are to propose a pattern classification method for remote sensing data using artificial neural network. First, we apply the error back propagation algorithm to classify the remote sensing data. In this case, the classification performance depends on a training data set. Using the training data set and the error back propagation algorithm, a layered neural network is trained such that the training pattern are classified with a specified accuracy. After training the neural network, some pixels are deleted from the original training data set if they are incorrectly classified and a new training data set is built up. Once training is complete, a testing data set is classified by using the trained neural network. The classification results of Landsat TM data show that this approach produces excellent results which are more realistic and noiseless compared with a conventional Bayesian method.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.705-719
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• 2010

We investigated the speckle filtering effect in supervised classification of the C-band polarimetric Ground Based SAR image data. Wishart classification method was used for the supervised classification of the polarimetric GB-SAR image data and total of 6 kinds of speckle filters were applied before supervised classification, which are boxcar, Gaussian, Lopez, IDAN, the refined Lee, and the refined Lee sigma filters. For each filters, we changed the filtering kernel size from $3{\times}3$ to $9{\times}9$ to investigate the filtering size effect also. The refined Lee filter with the kernel size of bigger than $5{\times}5$ showed the best result for the Wishart supervised classification of polarimetric GB-SAR image data. The result also showed that the type of trees could be discriminated by Wishart supervised classification of polarimetric GB-SAR image data.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.2
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• pp.163-172
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• 2001

This study present the quadratic programming as the classification method of remote sensed data applying to the extraction of landcover and examine it's applicable capability by comparing the classification accuracy of quadratic programming with that of neural network and maximum likelihood method which are used in the extraction of thematic layer. As the results, as drawing the more improved classification results by 6% than maximum likelihood method, we could discern that the method of quadratic programming is appliable to classifying the remote sensed data. Also, in the classification of quadratic programming method, we could definitely indicate the results which was ignored in the previous extreme(binary) classification method by affecting the class decision with the class composition proportion.

• Korean Journal of Remote Sensing
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• v.17 no.3
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• pp.189-197
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• 2001

Generally, remote sensing is useful to obtain the quantitative and qualitative information of a wide area. For monitoring earth resources and environment, land cover classification of remotely sensed data are needed over increasingly larger area. The objective this study is to propose the process for land cover classification method over a wide area using multi-scene satellite data. Land cover of Korean peninsula was extracted from a Landsat TM and ETM+ mosaic created from 23 scenes at 100-meter resolution. Well-known techniques that used to general image processing and classification are applied to this wide area classification. It is expected that these process is very useful to promptly and efficiently grasp of small scale spatial information such as national territorial information.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.637-639
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• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.969-969
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• 2003

• Management & Information Systems Review
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• v.1
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• pp.401-412
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• 1997

Traditionally, classification of remote sensed image data is one of the important works for image data analysis procedure. So, many researchers have been devoted their endeavor to increasing accuracy of analysis, also, many classification algorithms have been proposed. In this paper, we propose new bands selection method for multispectral bands of remote sensed image data that use rough set theory. Using indiscernibility relations in rough sets, we show that can select the efficient bands of multispectral image data, automatically.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.383-396
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• 2004

The application of spatial statistics to obtain the spatial uncertainty distributions in classification of remote sensing images is investigated in this paper. Two quantitative methods are presented for describing two kinds of uncertainty; one related to class assignment and the other related to the connection of reference samples. Three quantitative indices are addressed for the first category of uncertainty. Geostatistical simulation is applied both to integrate the exhaustive classification results with the sparse reference samples and to obtain the spatial uncertainty or accuracy distributions connected to those reference samples. To illustrate the proposed methods and to discuss the operational issues, the experiment was done on a multi-sensor remote sensing data set for supervised land-cover classification. As an experimental result, the two quantitative methods presented in this paper could provide additional information for interpreting and evaluating the classification results and more experiments should be carried out for verifying the presented methods.

• Management & Information Systems Review
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• v.3
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• pp.15-45
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• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.