• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.262-266
• /
• 1999

Several commercial companies that plan to provide improved panchromatic and/or multi-spectral remote sensor data in the near future are suggesting that merge datasets will be of significant value. This study evaluated the utility of one major merging process-process components analysis and its inverse. The 6 bands of 30$\times$30m Landsat TM data and the 10$\times$l0m SPOT panchromatic data were used to create a new 10$\times$10m merged data file. For the image classification, 6 bands that is 1st, 2nd, 3rd, 4th, 5th and 7th band may be used in conjunction with supervised classification algorithms except band 6. One of the 7 bands is Band 6 that records thermal IR energy and is rarely used because of its coarse spatial resolution (120m) except being employed in thermal mapping. Because SPOT panchromatic has high resolution it makes 10$\times$10m SPOT panchromatic data be used to classify for the detailed classification. SPOT as the Landsat has acquired hundreds of thousands of images in digital format that are commercially available and are used by scientists in different fields. After the merged, the classifications used supervised classification and neural network. The method of the supervised classification is what used parallelepiped and/or minimum distance and MLC(Maximum Likelihood Classification) The back-propagation in the multi-layer perception is one of the neural network. The used method in this paper is MLC(Maximum Likelihood Classification) of the supervised classification and the back-propagation of the neural network. Later in this research SPOT systems and images are compared with these classification. A comparative analysis of the classifications from the TM and merged SPOT/TM datasets will be resulted in some conclusions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.23 no.3
• /
• pp.313-322
• /
• 2005

Rapid advances in technology and changes in human and cultural activities bring about changes to the earth surface in terms of spatial extension as well as time frame of the changes. Such advances introduce shorter updating frequency of maps and geospatial database. To satisfy these requirements, recent research efforts in the geoinformatics field have been focused on the automation and speeding up of the mapping processes which resulted in products such as the digital photogrammetric workstation, GPSIINS, applications of satellite imagery, automatic feature extraction and the LiDAR system. The possibility of automatically extracting buildings and generating contours from airborne LiDAR data has received much attention because LiDAR data produce promising results. However, compared with the manually derived building footprints using traditional photogrammetric process, more investigation and analysis need to be carried out in terms of accuracy and efficiency. On the other hand, generation of the contours with LiDAR data is more efficient and economical in terms of the quality and accuracy. In this study, the effects of various conditions of the pre-processing phase and the subsequent building extraction and contour generation phases for digital mapping have on the accuracy were investigated.

• Korean Journal of Remote Sensing
• /
• v.13 no.1
• /
• pp.57-73
• /
• 1997

This paper is related to the correction of radiometric distortions induced by topographic relief. RADARSAT SAR image data were obtained over the mountainous area near southern part of Seoul. Initially, the SAR data was geometrically corrected and registered to plane rectangular coordinates so that each pixel of the SAR image has known topographic parameters. The topographic parameters (slope and aspect) at each pixel position were calculated from the digital elevation model (DEM) data having a comparable spatial resolution with the SAR data. Local incidence angle between the incoming microwave and the surface normal to terrain slope was selected as a primary geometric factor to analyze and to correct the radiometric distortions. Using digital maps of forest stands, several fields of rather homogeneous forest stands were delineated over the SAR image. Once the effects of local incidence angle on the radar backscatter were defined, the radiometric correction was performed by an empirical fuction that was derived from the relationship between the geometric parameters and mean radar backscatter. The correction effects were examined by ground truth data.

• Journal of Korea Water Resources Association
• /
• v.53 no.12
• /
• pp.1059-1070
• /
• 2020

River cross-section measurement data is one of the most important input data in research related to hydraulic and hydrological modeling, such as flow calculation and flood forecasting warning methods for river management. However, the acquisition of accurate and continuous cross-section data of rivers leading to irregular geometric structure has significant limitations in terms of time and cost. In this regard, a primary objective of this study is to develop a methodology that is able to measure the spatial distribution of continuous river characteristics by minimizing the input of time, cost, and manpower. Therefore, in this study, we tried to examine the possibility and accuracy of continuous cross-section estimation by estimating the water depth for each cross-section through multiple linear regression analysis using RGB-based aerial images and actual data. As a result of comparing with the actual data, it was confirmed that the depth can be accurately estimated within about 2 m of water depth, which can capture spatially heterogeneous relationships, and this is expected to contribute to accurate and continuous river cross-section acquisition.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.4
• /
• pp.45-53
• /
• 2013

Global warming causes sea levels to rise and global changes apparently taking place including coastline changes. Coastline change due to sea level rise is also one of the most significant phenomena affected by global climate change. Accordingly, Coastline change detection can be utilized as an indicator of representing global climate change. Generally, Coastline change has happened mainly because of not only sea level rise but also artificial factor that is reclaimed land development by mud flat reclamation. However, Arctic coastal areas have been experienced serious change mostly due to sea level rise rather than other factors. The purposes of this study are automatic extraction of coastline and identifying change. In this study, in order to extract coastline automatically, contrast of the water and the land was maximized utilizing modified NDWI(Normalized Difference Water Index) and it made automatic extraction of coastline possibile. The imagery converted into modified NDWI were applied image processing techniques in order that appropriate threshold value can be found automatically to separate the water and land. Then the coastline was extracted through edge detection algorithm and changes were detected using extracted coastlines. Without the help of other data, automatic extraction of coastlines using LANDSAT was possible and similarity was found by comparing NLCD data as a reference data. Also, the results of the study area that is permafrost always frozen below $0^{\circ}C$ showed quantitative changes of the coastline and verified that the change was accelerated.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.15 no.4
• /
• pp.1-14
• /
• 2012

A river is defined as the watercourse flowing through its channel, and the mapping tasks of a river plays an important role for the research on the topographic changes in the riparian zones and the research on the monitoring of flooding in its floodplain. However, the utilization of the ground surveying technologies is not efficient for the mapping tasks of a river due to the irregular surfaces of the riparian zones and the dynamic changes of water level of a river. Recently, the spatial information data sets are widely used for the coastal mapping tasks due to the acquisition of the topographic information without human accessibility. In this research, we tried to extract a river from the RapidEye imagery by using the ISODATA(Iterative Self_Organizing Data Analysis) classification algorithm with the two different parameters(NIR (Near Infra-Red) band and NDVI(Normalized Difference Vegetation Index)). First, the two different images(the NIR band image and the NDVI image) were generated from the RapidEye imagery. Second, the ISODATA algorithm were applied to each image and each river was generated in each image through the post-processing steps. River boundaries were also extracted from each classified image using the Sobel edge detection algorithm. Ground truths determined by the experienced expert are used for the assessment of the accuracy of an each generated river. Statistical results show that the extracted river using the NIR band has higher accuracies than the extracted river using the NDVI.

• Korean Journal of Remote Sensing
• /
• v.22 no.6
• /
• pp.553-563
• /
• 2006

This study aims to produce land-cover maps of Korean peninsula using multi-temporal MODIS (Moderate Resolution Imaging Spectroradiometer) imagery. To solve the low spatial resolution of MODIS data and enhance classification accuracy, Linear Spectral Mixture Analysis (LSMA) was employed. LSMA allowed to determine the fraction of each surface type in a pixel and develop vegetation, soil and water fraction images. To eliminate clouds, MVC (Maximum Value Composite) was utilized for vegetation fraction and MinVC (Minimum Value Composite) for soil fraction image respectively. With these images, using ISODATA unsupervised classifier, southern part of Korean peninsula was classified to low and mid level land-cover classes. The results showed that vegetation and soil fraction images reflected phenological characteristics of Korean peninsula. Paddy fields and forest could be easily detected in spring and summer data of the entire peninsula and arable land in North Korea. Secondly, in low level land-cover classification, overall accuracy was 79.94% and Kappa value was 0.70. Classification accuracy of forest (88.12%) and paddy field (85.45%) was higher than that of barren land (60.71%) and grassland (57.14%). In midlevel classification, forest class was sub-divided into deciduous and conifers and field class was sub-divided into paddy and field classes. In mid level, overall accuracy was 82.02% and Kappa value was 0.6986. Classification accuracy of deciduous (86.96%) and paddy (85.38%) were higher than that of conifers (62.50%) and field (77.08%).

• Korean Journal of Remote Sensing
• /
• v.39 no.2
• /
• pp.183-192
• /
• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.3
• /
• pp.1-18
• /
• 2018

The purpose of this study is to analyze sensitivity of forest ecosystem to climate change using spatial analysis methods focused on 6 national parks. To analyze, we constructed MODIS NDVI and temperature of Korea Meteorologic Administration based on 1km spatial resolution and 16 days. And we conducted time-series and correlation analysis using MODIS NDVI and temperature. A most sensitive region to climate change is Jirisa National Park(r=0.434) and Seoraksan National Park(r=0.415), there is the highest mean correlation coefficient. The sensitivity of forest ecosystem varied according to habitat characteristics and forest types in national park. In Abies koreana of Hallsan Nation Park, temperature has raised, but NDVI has decreased. these results will be based data of climate change adaption policy for protecting forest ecosystem.

• Spatial Information Research
• /
• v.23 no.2
• /
• pp.31-38
• /
• 2015

Remote sensing data is used to increasing efficiency on benthic cover type survey. Satellite and aerial imagery has variance of reflectance by water column effect even if bottom is consisted with same cover type and condition. This study tried to analyze advances of surveying extent and accuracy through water column correction of CASI-1500 hyperspectral image. Study area is coast of Gangneung city, South Korea where benthic environment is rapidly changing with bleaching of coral reef. Water column correction coefficient was estimated using regression models between water reflectance ($R_W$) and depth for sand bottom then the coefficients were applied to whole image. The results shows that expanded interpretable depth from 6-7m to 15m and decreased variation of reflectance by depth. Additionally, water column corrected reflectance image shows 13%p increased accuracy on benthic cover type classification.