• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.61-66
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• 2003

The change detection using the satellite imagery and airphotos has been carried out in the application of terrain mapping, environment, forestry, facility detection, etc. The low-spatial resolution data such as Landsat, NOAA satellite images is generally used for automatic change detection, while on the other hand the high-spatial resolution data is used for change detection by image interpretation. The research to integrate automatic method with manual change detection through the high-spatial resolution satellite image is performed. but the problem such as shadow, building 'lean' due to perspective geometry and precision geocorrection was found. In this paper we performed change detection using the IKONOS satellite images, and present the concerning problem.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.204-209
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• 1999

In general, neural networks are widely used for the category classification. But when low contrast images, such as multispectral images, are used as the input of neural networks, neural networks converge very slowly and are of bad performance. To overcome this problem, we propose a new image enhancement method which consists of smoothing process, finding the main valley and enhancement process. And the enhanced images by the proposed method are used as the input of neural networks for the category classification. When the new category classification method is applied to multispectral LANDSAT TM images, we verified that neural networks converge very fast and overall category classification performance is improved.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.5
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• pp.493-504
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• 2010

This study proposes a classification method based on an automated training extraction procedure that may be used with very high resolution (VHR) images of non-accessible areas. The proposed method overcomes the problem of scale difference between VHR images and geographic information system (GIS) data through filtering and use of a Landsat image. In order to automate maximum likelihood classification (MLC), GIS data were used as an input to the MLC of a Landsat image, and a binary edge and a normalized difference vegetation index (NDVI) were used to increase the purity of the training samples. We identified the thresholds of an NDVI and binary edge appropriate to obtain pure samples of each class. The proposed method was then applied to QuickBird and SPOT-5 images. In order to validate the method, visual interpretation and quantitative assessment of the results were compared with products of a manual method. The results showed that the proposed method could classify VHR images and efficiently update GIS data.

• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.71-79
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• 2002

This research presents particulate distribution map of tidal flats of Hampyung bay using reflectance which extracted from satellite data and field survey data during same periods. The spectrum of particulate composition obtained from Landsat TM data was analysed and 7 scenes of satellite image were classified with ISODATA and K-MEANS methods. The results of unsupervised classification were estimated with in-situ data. The classification accuracy of ISODATA and K-MAMS methods were 84.3% and 85.7%. For validation of classified results of multi-temporal satellite images, TM image of May 1999(reference data), which was classified with field survey data was compared with classified results of multi-temporary satellite data.

• Korean Journal of Remote Sensing
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• v.8 no.2
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• pp.131-145
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• 1992

A low cost personal computer and image processing S/W were empolyed to derive Digtal Elevation Model(DEM) of tidal flat from multitemporal LANDSAT TM images, and to create three-dimensional(3D) perspective views of the tidel flat on Komso bay in west coasts of Korea. The method for generation of Digital Elevation Model(DEM) in tidal flat was considered by overlapping techniques of multitemporal LANDSAT TM images and interpolations. The boundary maps of tidal flat extracted from multitemporal images with different water high were digitally combined in x, y, z space with tide in formation and used as an inputcontour data to obtain an elevation model by interpolation using spline function. Elevation errors of less than $\pm$0.1m were achived using overlapping techniques and a spline interpolation approach, respectively. The derived DEM allows for the generation of a perspective grid and drape on the satellite image values to create a realistic terrain visualization model so that the tidal flat may be viewed from and desired direction. As the result of this study, we obtained elevation model of tidal flats which contribute to characterize of topography and monitoring of morphological evolution of tidal flats. Moreover, the modal generated here can be used for simulation of innudation according to tide and support other studies as a supplementary data set.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1464-1466
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• 2003

classification of the satellite images provides information about land cover and/or land use. Quality of the classification result depends mainly on the spatial and spectral resolutions of the images. In this study, image fusion in terms of resolution merging, and band integration with multi-source of the satellite images; Landsat ETM+ and Ikonos were carried out to improve classification. Resolution merging and band integration could generate imagery of high resolution with more spectral bands. Precise image co-registration is required to remove geometric distortion between different sources of images. Combination of unsupervised and supervised classification of the fused imagery was implemented to improve classification. 3D display of the results was possible by combining DEM with the classification result so that interpretability could be improved.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.449-461
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• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.

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

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.671-674
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• 2005

Atmospheric correction over the ocean part is more important than that over the land because the signal from the ocean is very small about one tenth of that reflected from land. In this study, the Spectral Shape Matching Method (SSMM) developed by Ahn and Shanmugam (2004) is evaluated using Landsat imagery acquired over the highly turbid Saemangeum Coastal Area. The result of SSMM is compared with COST model developed by Chavez (1991 and 1997). In principle, SSMM is simple and easy to implement on any satellite imagery, relying on both field and image properties. To assess the potential use of these methods, several field campaigns were conducted in the Saemangeum coastal area corresponding with Landsat-7 satellite's overpass on 29 May 2005. In-situ data collected from the coastal waters of Saemangeum using optical instruments (ASD field spectroradiometer) consists of ChI, Ap, SS, aooM, F(d). In order to perform SSMM, we use the in-situ water-leaving radiance spectra from clear oceanic waters to estimate the the path radiance from total signal recorded at the top of the atmosphere (TOA), due to the reason that the shape of clear water-leaving radiance spectra is nearly stable than turbid water-leaving radiance spectra. The retrieved water-leaving radiance after subtraction of path signal from TOA signal in this way is compared with that estimated by COST model. The result shows that SSMM enabled retrieval of water-leaving radiance spectra that are consistent with in-situ data obtained from Saemangeum coastal waters. The COST model yielded significantly high errors in these areas.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.441-444
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• 2005

The goal of this study is to investigate the relationship between microphytobenthic biomass and normalized vegetation index obtained from Landsat TM images. Monitoring a seasonal change of microphytobenthic biomass in the sand bar is specifically focused. Since the study area, Hampyeong Bay, was difficult to approach, we failed to obtain ground truths simultaneously on satellite image acquisition. Instead, chlorophyll-a concentration in surface top layer was measured on different dates for microphytobenthic biomass. Although data were acquired on different dates, a correlation between the field and satellite images was calculated for investigating general trends of seasonal change. NDVI and tasseled cap transformed images were also used to review the variation of microphytobenthic biomass by using Landsat TM and ETM+ images. Atmosphere effects were corrected by applying COST model. Seaweeds were also flouring in the same season of microphytobentic blooming. Songseok-ri area was minimally affected by seaweeds from February to May, and selected as a test site. NDVI value was classified into high-, moderate-, and low-grade. It was well developed over fme-grained sediments and rapidly reduced from May to November over sand bar. In this bay, correlation between grain size and microphytobenthic biomass was clearly seen. From the classified NDVI and tasseled cap transformed data, we finally constructed spatial distribution and seasonal variation maps of microphytobenthic biomass.