• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1945-1953
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• 2022

Korea Aerospace Research Institute produces mosaic images of the Korean Peninsula every year to promote the use of satellite images and provides them to users in the public sector. However, since the pan-sharpening and color balancing methodologies are applied during the mosaic image processing, the original spectral information is distorted. In addition, there is a limit to analyze using mosaic images as mosaic images provide only Red, Green and Blue bands excluding Near Infrared (NIR) band. Therefore, in order to compensate for these limitations, this study applied the Principal Component Analysis (PCA) technique and indices extracted from R, G, B bands together for image classification and compared the classification results. As a result of the analysis, the accuracy of the mosaic image classification result was about 67.51%, while the accuracy of the image classification result using both PCA and RGB indices was about 75.86%, confirming that the accuracy of the image classification result can be improved. As a result of comparing the PCA and the RGB indices, the accuracy of the image classification result was about 64.10% and 74.05% respectively. Through this, it was confirmed that the classification accuracy using the RGB indices was higher among the two techniques, and implications were derived that it was important to use high quality reference or supplementary data. In the future, additional indices and techniques are needed to improve the classification and analysis results of mosaic images, and related research is expected to increase the utilization of images that provide only R, G, B or limited spectral information.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.511-521
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• 2022

Cloud detection and removal from satellite images is an essential process for topographic observation and analysis. Threshold-based cloud detection techniques show stable performance because they detect using the physical characteristics of clouds, but they have the disadvantage of requiring all channels' images and long computational time. Cloud detection techniques using deep learning, which have been studied recently, show short computational time and excellent performance even using only four or less channel (RGB, NIR) images. In this paper, we confirm the performance dependence of the deep learning network according to the heterogeneous learning dataset with different resolutions. The DeepLabV3+ network was improved so that channel features of cloud detection were extracted and learned with two published heterogeneous datasets and mixed data respectively. As a result of the experiment, clouds' Jaccard index was low in a network that learned with different kind of images from test images. However, clouds' Jaccard index was high in a network learned with mixed data that added some of the same kind of test data. Clouds are not structured in a shape, so reflecting channel features in learning is more effective in cloud detection than spatial features. It is necessary to learn channel features of each satellite sensors for cloud detection. Therefore, cloud detection of heterogeneous sensors with different resolutions is very dependent on the learning dataset.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.571-585
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• 2022

As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.695-706
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• 2022

Medium and high-resolution optical satellites have proven their effectiveness in detecting wildfire areas. However, smoke plumes generated by wildfire scatter visible light incidents on the surface, thereby interrupting accurate monitoring of the area where wildfire occurs. Therefore, a technology to extract smoke in advance is required. Deep learning technology is expected to improve the accuracy of smoke extraction, but the lack of training datasets limits the application. However, for clouds, which have a similar property of scattering visible light, a large amount of training datasets has been accumulated. The purpose of this study is to develop a smoke extraction technique using deep learning, and the limits due to the lack of datasets were overcome by using a cloud dataset on transfer learning. To check the effectiveness of transfer learning, a small-scale smoke extraction training set was made, and the smoke extraction performance was compared before and after applying transfer learning using a public cloud dataset. As a result, not only the performance in the visible light wavelength band was enhanced but also in the near infrared (NIR) and short-wave infrared (SWIR). Through the results of this study, it is expected that the lack of datasets, which is a critical limit for using deep learning on smoke extraction, can be solved, and therefore, through the advancement of smoke extraction technology, it will be possible to present an advantage in monitoring wildfires.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.177-185
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• 2008

Accurate monitoring of snow cover is a key component for studying climate and global as well as for daily weather forecasting and snowmelt runoff modelling. The few observed data related to snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) were built for 7 major watersheds in South Korea. The decrease pattern of SCA for time (day) was expressed as exponentially decay function, and the determination coefficient was ranged from 0.46 to 0.88. The SCA decreased 70% to 100% from the maximum SCA when 10 days passed.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1747-1761
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• 2023

Wetlands, defined as lands periodically inundated or exposed during the year, are crucial for sustaining biodiversity and filtering environmental pollutants. The importance of mapping and monitoring their topographical changes is therefore paramount. This study focuses on the topographical variations at the Incheon River estuary wetland post-restoration, noting a lack of adequate prior measurements. Using a multi-temporal Sentinel-1 dataset from October 2014 to March 2023, we mapped long-term variations in water bodies and detected topographical change anomalies using a pixel-frequency approach. Our analysis, based on 196 Sentinel-1 acquisitions from an ascending orbit, revealed significant topography changes. Since 2020, employing the pixel-frequency technique, we observed area increases of +0.0195, 0.0016, 0.0075, and 0.0163 km² in water level sections at depths of 2-3 m, 1-2 m, 0-1 m, and less than 0 m, respectively. These findings underscore the effectiveness of the wetland restoration efforts in the area.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.4
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• pp.1-9
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• 2015

The development of drought index that provides detailed-spatial-resolution drought information is essential for improving drought planning and preparedness. The objective of this study was to develop the concept of using satellite-based hybrid drought index called the Vegetation Drought Response Index in South Korea (VegDRI-SKorea) that could improve spatial resolution for monitoring local and regional drought. The VegDRI-SKorea was developed using the Classification And Regression Trees (CART) algorithm based on remote sensing data such as Normalized Difference Vegetation Index (NDVI) from MODIS satellite images, climate drought indices such as Self Calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Index (SPI), and the biophysical data such as land cover, eco region, and soil available water capacity. A case study has been done for the 2012 drought to evaluate the VegDRI-SKorea model for South Korea. The VegDRI-SKorea represented the drought areas from the end of May and to the severe drought at the end of June. Results show that the integration of satellite imageries and various associated data allows us to get improved both spatially and temporally drought information using a data mining technique and get better understanding of drought condition. In addition, VegDRI-SKorea is expected to contribute to monitor the current drought condition for evaluating local and regional drought risk assessment and assisting drought-related decision making.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.6
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• pp.627-636
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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 to consider spectral and spatial information of high resolution satellite image. Firstly, the initial seeds were automatically selected using local variation of multi-spectral edge information. After automatic selection of significant seeds, a segmentation was achieved by applying MSRG which determines the priority of region growing using information drawn from similarity between the extracted each seed and its neighboring points. In order to evaluate the performance of the proposed method, the results obtained using the proposed method were compared with the results obtained using conventional region growing and watershed method. The quantitative comparison was done using the unsupervised objective evaluation method and the object-based classification result. Experimental results demonstrated that the proposed method has good potential for application in the object-based analysis of high resolution satellite images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.471-478
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• 2011

High resolution Pan-sharpening technique is becoming increasingly important in the field of remote sensing image analysis as an essential image processing to improve the spatial resolution of original multispectral image. The general scheme of pan-sharpening technique consists of upsampling process of multispectral image and high-pass detail injection process using the panchromatic image. The upsampling process, however, brings about image blurring, and this lead to spectral distortion in the pan-sharpening process. In order to solve this problem, this paper presents a new method that adopts image restoration techniques based on optimization theory in the pan-sharpening process, and evaluates its efficiency and application possibility. In order to evaluate the effect of image restoration techniques on the pansharpening process, the result obtained using the existing method that used bicubic interpolation were compared visually and quantitatively with the results obtained using image restoration techniques. The quantitative comparison was done using some spectral distortion measures for use to evaluate the quality of pan-sharpened image.

• Journal of Advanced Navigation Technology
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• v.2 no.2
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• pp.132-142
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• 1998

In optical scanning holography, 3-D holographic information of an object is generated by 2-D active optical scanning. The optical scanning beam can be a time-dependent Gaussian apodized Fresnel zone plate. In this technique, the holographic information manifests itself as an electrical signal which can be sent to an electron-beam-addressed spatial light modulator for coherent image reconstruction. This technique can be applied to 3-D optical remote sensing especially for identifying flying objects. In this paper, we first briefly review optical scanning holography and analyze the resolution achievable with the system. We then present mathematical expression of real and virtual image which are responsible for holographic image reconstruction by using Gaussian beam profile.