• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• Spatial Information Research
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• v.8 no.2
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• pp.275-288
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• 2000

The purpose of this study is to analyze relationship between the NDVI change pattern and landcover change pattern in the City of Sungnam during 1985 and 1996. The results of this study are as follows; (1) NDVI of the level 6 and 7 is decreased and the level 5 is increased in the area where Forst area changed to the other land cover during 1985 and 1996. (2) In the area where Agricultural-Pasture changed to forest, NDVI level became higher certainly during that time. But in the area where there has been changed from Agricultural-Pasture to Urban or built-up, Agricultural-Pasture to Barren land, the level of NDVI is decreased. (3) In the Urban or built-up to other land, or built-up the level of NDVI is increased. (4) In the area where Barren land changed to other land cover, the level of NDVI is increased.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.301-308
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• 2022

Algae is an indispensable primary producer in the ecosystem by supplying energy to consumers in the aquatic ecosystem, and is largely divided into green algae, blue-green algae, and diatoms. In the case of blue-green algae, the water temperature rises, which occurs in the summer and overgrows, which is the main cause of the algae bloom. Recently, the change in the occurrence time and frequency of the algae bloom is increasing due to climate change. Existing algae survey methods are performed by collecting water and measuring through sensors, and time, cost and manpower are limited. In order to overcome the limitations of these existing monitoring methods, research has been conducted to perform remote monitoring using spectroscopic devices such as multispectral and hyperspectral using satellite image, UAV, etc. In this study, we tried to confirm the possibility of species classification of remote monitoring through laboratory-scale experiments through algal culture and river water collection. In order to acquire hyperspectral images, a hyperspectral sensor capable of analyzing at 400-1000 nm was used. In order to extract the spectral characteristics of the collected river water for classification of algae species, filtration was performed using a GF/C filter to prepare a sample and images were collected. Radiation correction and base removal of the collected images were performed, and spectral information for each sample was extracted and analyzed through the process of extracting spectral information of algae to identify and compare and analyze the spectral characteristics of algae, and remote sensing based on hyperspectral images in rivers and lakes. We tried to review the applicability of monitoring.

• Spatial Information Research
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• v.15 no.1
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• pp.25-33
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• 2007

The purpose of this study is to assess spatio-temporal variation of Imha Dam water quality according to suspended sediment algorithm using Landsat and SPOT 5 data. In order to learn synchronous suspended sediment concentrations(SSC) in Imha Dam waters, the satellite remote sensing data are analyzed. The key procedure of this research is that we should know the relationships between suspended sediment concentrations and satellite-detected reflectance. However, the SSC algorithm has the limitation that it must be compared calculated SSC with synchronous ground-truth data in the Dam water. Based upon the linear response from satellite-detected reflectance, SSC algorithm validated an efficient algorithm to estimate proportional factor and then derived an empirical equation far SSC estimations.

• Journal of the Korean Geosynthetics Society
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• v.21 no.4
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• pp.55-67
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• 2022

In this paper, fundamentals and recent development of the interferometric synthetic aperture radar, known as InSAR, technique for measuring ground deformation through satellite image analysis are presented together with case histories illustrating its applicability to urban ground deformation monitoring. A study area in Korea was selected and processed based on the muti-temporal time series InSAR analysis, namely SBAS (Small Baseline Subset)-InSAR and PS (Persistent Scatterers)-InSAR using Sentinel-1A SAR images acquired from the year 2014 onward available from European Space Agency Copernicus Program. The ground settlement of the study area for the temporal window of 2014-2022 was evaluated from the viewpoint of the applicability of the InSAR technique for urban infrastructure settlement monitoring. The results indicated that the InSAR technique can reasonably monitor long-term settlement of the study area in millimetric scale, and that the time series InSAR technique can effectively measure ground settlement that occurs over a long period of time as the SAR satellite provides images of the Korean Peninsula at regular time intervals while orbiting the earth. It is expected that the InSAR technique based on higher resolution SAR images with small temporal baseline can be a viable alternative to the traditional ground borne monitoring method for ground deformation monitoring in the 4^th industrial era.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.317-326
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• 2019

Salinity which is often detected on reclaimed land is a major detrimental factor to crop growth. It would be advantageous to develop an approach for assessment of salinity and drought damages using a non-destructive method in a large landfills area. The objective of this study was to examine applicability of the decision tree classifier using imagery for classifying for spring potatoes (Solanum tuberosum) damaged by salinity or drought at vegetation growth stages. We focused on comparing the accuracies of OA (Overall accuracy) and KC (Kappa coefficient) between the simple reflectance and the band ratios minimizing the effect on the light unevenness. Spectral merging based on the commercial band width with full width at half maximum (FWHM) such as 10 nm, 25 nm, and 50 nm was also considered to invent the multispectral image sensor. In the case of the classification based on original simple reflectance with 5 nm of FWHM, the selected bands ranged from 3-13 bands with the accuracy of less than 66.7% of OA and 40.8% of KC in all FWHMs. The maximum values of OA and KC values were 78.7% and 57.7%, respectively, with 10 nm of FWHM to classify salinity and drought damages of spring potato. When the classifier was built based on the band ratios, the accuracy was more than 95% of OA and KC regardless of growth stages and FWHMs. If the multispectral image sensor is made with the six bands (the ratios of three bands) with 10 nm of FWHM, it is possible to classify the damaged spring potato by salinity or drought using the reflectance of images with 91.3% of OA and 85.0% of KC.

• Journal of The Geomorphological Association of Korea
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• v.18 no.1
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• pp.41-56
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• 2011

This study analyzes the change in land-cover based on satellite imagery to draw up land-cover map in the future, and estimates the change in land category using statistical data of the land category. To estimate land category, this study applied the double exponentially smoothing method. The result of the land cover classification according to year using satellite imagery showed that the type with the largest increase in area of land cover change in the cities of Cheonan and Asan was artificial structure, followed by water, grass field and bare land. However forest, paddy, marsh and dry field were reduced. Further, the result of the time-series analysis of the land category was found to be similar to the result of the land cover classification using satellite imagery. Especially, the result of the estimation of the land category change using the double exponentially smoothing method showed that paddy, dry field, forest and marsh are anticipated to consistently decrease in area from 2010 to 2100, whereas artificial structure, water, bare land and grass field are anticipated to consistently increase. Such results can be utilized as basic data to estimate the change in land cover according to climate change in order to prepare climate change response strategies.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.11-17
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• 2013

With high social demands for the diverse utilizations of forest lands, the illegal forest land use changes have increased. We studied change detection technique to detect changes in forest land use using an object-oriented segmentation of RED bands differencing in multi-temporal aerial photographs. The new object-oriented segmentation method consists of the 5 steps, "Image Composite - Segmentation - Reshaping - Noise Remover - Change Detection". The method enabled extraction of deforested objects by selecting a suitable threshold to determine whether the objects was divided or merged, based on the relations between the objects, spectral characteristics and contextual information from multi-temporal aerial photographs. The results found that the object-oriented segmentation method detected 12% of changes in forest land use, with 96% of the average detection accuracy compared by visual interpretation. Therefore this research showed that the spatial data by the object-oriented segmentation method can be complementary to the one by a visual interpretation method, and proved the possibility of automatically detecting and extracting changes in forest land use from multi-temporal aerial photographs.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.175-181
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• 2022

The condition of infrastructure deteriorates as the service life increases. Since most infrastructure in South Korea were intensively built during the period of economic growth, the proportion of outdated infrastructure is rapidly increasing now. Aging of such infrastructure can lead to safety accidents and even human casualties. To prevent these issues in advance, periodic and accurate inspection is essential. For this reason, the need for research to detect various types of damage using computer vision and deep learning is increasingly required in the field of remotely controlled or autonomous inspection. To this end, this study proposed a neural network structure that can detect concrete damage by classifying it into three types. In particular, the proposed neural network can detect them more accurately through a hierarchical learning technique. This neural network was trained with 2,026 damage images and tested with 508 damage images. As a result, we completed an algorithm with average mean intersection over union of 67.04% and F1 score of 52.65%. It is expected that the proposed damage detection algorithm could apply to accurate facility condition diagnosis in the near future.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.737-750
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• 2019

Information of target changes in inaccessible areas is very important in terms of national security. Fast and accurate change detection of targets is very important to respond quickly. Spaceborne synthetic aperture radar can acquire images with high accuracy regardless of weather conditions and solar altitude. With the recent increase in the number of SAR satellites, it is possible to acquire images with less than one day temporal resolution for the same area. This advantage greatly increases the availability of change detection for inaccessible areas. Commonly available information in satellite SAR is amplitude and phase information, and change detection techniques have been developed based on each technology. Those are amplitude Change Detection (ACD), Coherence Change Detection (CCD). Each algorithm differs in the preprocessing process for accurate automatic classification technique according to the difference of information characteristics and the final detection result of each algorithm. Therefore, by analyzing the academic research trends for ACD and CCD, each technologies can be complemented. The goal of this paper is identifying current issues of SAR change detection techniques by collecting research papers. This study would help to find the prerequisites for SAR change detection and use it to conduct periodic detection research on inaccessible areas.