• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.317-326
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• 2019

Development projects and related environmental impacts take place in space. Therefore, it is important to use spatial information in the environmental impact assessment process. This study proposes to construct spatial information produced by various organizations as an inventory and suggests it to be utilized in environmental impact assessment process. For this purpose, investigate the use of spatial information in the environmental impact assessment process and list of environmental space information provided by public information systems. and applied the methodology derived from previous studies to build an inventory of spatial information using environmental impact assessment. The spatial information utilized in the environmental impact assessment work was 64 items. Based on the data availability, linkage and renewability, the spatial information of the Environment that can be used for the environmental impact assessment was 45 items. Finally 49 items, including 19 new items were presented as an inventory, contributing to the performance of environmental impact assessment based on spatial information.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.267-279
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• 2015

In the development of autonomous vehicles, the importance of LIDAR sensors becomes larger. For sensor selection or algorithm development, it is difficult to test expensive LIDAR sensors mounted on a vehicle under various driving environment. In this study, we developed a simulator that is generally applicable for various vehicle LIDAR sensors based on the generalized geometric modeling of the common processes associated with vehicle LIDAR sensors. By configuring this simulator with the specific sensors being widely used, we performed the data simulation and quality analysis. Also, we applied the simulation data to obstacle detection and evaluated the applicability of the selected sensor. The developed simulator enables various experiments and algorithm development in parallel with hardware implementation prior to the deployment and operation of a sensor.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.169-172
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have been tried to enhance classification accuracy. Previous studies show that the classification technique based on wavelet transform is more effective than that of traditional techniques based on original pixel values, especially in complicated imagery. Various wavelets can be used in wavelet transform. Wavelets are used as basis functions in representing other functions, like sinusoidal function in Fourier analysis. In these days, some basis functions such as Haar, Daubechies, Coiflets and Symlets are mainly used in 2D image processing. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we computed the wavelet coefficients of satellite image using 10 different basis functions, and then classified test image. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis function. The energy parameter of signal is the sum of the squares of wavelet coefficients. The energy parameter is calculated by sub-bands after the wavelet decomposition and the energy parameter of each sub-band can be a favorable feature of texture. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• Journal of the Optical Society of Korea
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• v.20 no.6
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• pp.669-677
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• 2016

A miniaturized FTIR spectrometer based on lamellar grating interferometry is being developed for passive remote-sensing. Consisting of a pair of micro-mirror arrays, the lamellar grating can be fabricated using MEMS technology. This paper describes a method to compute the optical field in the interferometer to optimize the design parameters of the lamellar grating FTIR spectrometer. The lower limit of the micro-mirror width in the grating is related to the formation of a Talbot image in the near field and is estimated to be about $100{\mu}m$ for the spectrometer to be used for the wavelength range of $7-14{\mu}m$. In calculating the far field at the detection window, the conventional Fraunhofer equation is inadequate for detection distance of our application, misleading the upper limit of the micro-mirror width to avoid interference from higher order diffractions. Instead, the far field is described by the unperturbed plane-wave combined with the boundary diffraction wave. As a result, the interference from the higher order diffractions turns out to be negligible as the micro-mirror width increases. Therefore, the upper limit of the micro-mirror width does not need to be set. Under this scheme, the interferometer patterns and their FT spectra are successfully generated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.4
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• pp.421-430
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• 2010

Image segmentation technique is becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification to extract object regions of interest within images. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Improved Seeded Region Growing (ISRG) and Region merging. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained multi-spectral edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying ISRG to consider spectral and edge information. Finally the region merging process, integrating region texture and spectral information, was carried out to get the final segmentation result. The accuracy assesment was done using the unsupervised objective evaluation method for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.25-36
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• 2000

To evaluate the feasibility of using lineaments for the interpretation of regional geological structures, the extracted lineaments from satellite image and surveyed surface geological features mapped in the field were analyzed for the Euiseong Sub-basin. The lineaments extracted from Landsat-5 TM images show primary directions of N20$^{\circ}$~30$^{\circ}$E, N60$^{\circ}$~70$^{\circ}$E, N60$^{\circ}$~70$^{\circ}$W, which represent the trends of faults, strikes, and joints. In the sedimentary formation in the northern part of Palgongsan Uplift Zone, primary directions of the lineaments are NNE and NWW, and NEE in southern parts. The analysis of satellite lineaments is proved to be very useful to study the large-scale structures and surface geology of the Euiseong Sub-basin, whereas the previous research using brittle tectonics approach was advantaged in the outcrop scale in interpretation.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.287-295
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• 2007

A mineral potential map showing the distributions of potential areas for exploration of undiscovered mineral deposits is a kind of predictive thematic maps. For any predictive thematic maps to show reasonably significant prediction results, validation information on prediction capability should be provided in addition to spatial locations of high potential areas. The objective of this paper is to apply prediction rate curves to the estimation of prediction probability of future discovery. A case study for Au-Ag mineral potential mapping using geochemical data sets is carried out to illustrate procedures for estimating prediction probability and for an interpretation. Through the case study, quantitative information including prediction rates and probability obtained by prediction rate curves was found to be very important for the interpretation of prediction results. It is expected that such quantitative validation information would be effectively used as basic information for cost analysis of exploration and environmental impact assessment.

• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.455-464
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• 2009

Lee(2009) proposed the boundary-adaptive despeckling method using a Bayesian model which is based on the lognormal distribution for image intensity and a Markov random field(MRF) for image texture. This method employs the Point-Jacobian iteration to obtain a maximum a posteriori(MAP) estimate of despeckled imagery. The boundary-adaptive algorithm is designed to use less information from more distant neighbors as the pixel is closer to boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The boundary-adaptive scheme was comprehensively evaluated using simulation data and the effectiveness of boundary adaption was proved in Lee(2009). This study, as an extension of Lee(2009), has suggested a modified iteration algorithm of MAP estimation to enhance computational efficiency and to combine classification. The experiment of simulation data shows that the boundary-adaption results in yielding clear boundary as well as reducing error in classification. The boundary-adaptive scheme has also been applied to high resolution Terra-SAR data acquired from the west coast of Youngjong-do, and the results imply that it can improve analytical accuracy in SAR application.

• Korean Journal of Remote Sensing
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• v.15 no.1
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• pp.39-50
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• 1999

It is complicate and time-consuming process to classify a multi-band satellite imagery according to the application. In addition, classification rate sensitively depends on the selection of training data set and features in a supervised classification process. This paper introduced a classification network adopting a fuzzy-based $\gamma$-model in order to select a training data set and to extract feature which highly contribute to an actual classification. The features used in the classification were gray-level histogram, textures, and NDVI(Normalized Difference Vegetation Index) of target imagery. Moreover, in order to minimize the errors in the classification network, the Gradient Descent method was used in the training process for the $\gamma$-parameters at each code used. The trained parameters made it possible to know the connectivity of each node and to delete the void features from all the possible input features.