• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.357-367
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• 2012

This paper presents a two-stage methodology for anomaly detection from hyperspectral imagery that consists of transform-based feature extraction and selection, and computation of a local spatial auto-correlation statistic. First, principal component transform and 3D wavelet transform are applied to reduce redundant spectral information from hyperspectral imagery. Then feature selection based on global skewness and the portion of highly skewed sub-areas is followed to find optimal features for anomaly detection. Finally, a local indicator of spatial association (LISA) statistic is computed to account for both spectral and spatial information unlike traditional anomaly detection methodology based only on spectral information. An experiment using airborne CASI imagery is carried out to illustrate the applicability of the proposed anomaly detection methodology. From the experiments, anomaly detection based on the LISA statistic linked with the selection of optimal features outperformed both the traditional RX detector which uses only spectral information, and the case using major principal components with large eigen-values. The combination of low- and high-frequency components by 3D wavelet transform showed the best detection capability, compared with the case using optimal features selected from principal components.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1061-1073
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• 2017

In this study, images were classified using convolutional neural network (CNN) - a deep learning technique - to investigate the feasibility of information production through a combination of artificial intelligence and spatial data. CNN determines kernel attributes based on a classification criterion and extracts information from feature maps to classify each pixel. In this study, a CNN network was constructed to classify materials with similar spectral characteristics and attribute information; this is difficult to achieve by conventional image processing techniques. A Compact Airborne Spectrographic Imager(CASI) and an Airborne Imaging Spectrometer for Application (AISA) were used on the following three study sites to test this method: Site 1, Site 2, and Site 3. Site 1 and Site 2 were agricultural lands covered in various crops,such as potato, onion, and rice. Site 3 included different buildings,such as single and joint residential facilities. Results indicated that the classification of crop species at Site 1 and Site 2 using this method yielded accuracies of 96% and 99%, respectively. At Site 3, the designation of buildings according to their purpose yielded an accuracy of 96%. Using a combination of existing land cover maps and spatial data, we propose a thematic environmental map that provides seasonal crop types and facilitates the creation of a land cover map.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.507-519
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• 2017

The applications of NDVI (Normalized Difference Vegetation Index) as a vegetation index has been widely used to understand vegetation biomass and physiological activities. However, NDVI is not suitable way for monitoring vegetation stress because it is less sensitive to change in physiological state than biomass. PRI (Photochemical Reflectance Index) is well developed to present physiological activities of vegetation, particularly high-light-stress condition, and it has been adopted in several satellites to be launched in the future. Thus, the understanding of PRI performance and the development of analysis method will be necessary. This study aims to interpret the characteristics of light-stress-sensitive PRI in shadow areas and to evaluate the PRI calculated by other wavelengths (i.e., 488.9 nm, 553.6 nm, 646.9 nm, and 668.4 nm) instead of 570 nm that used in original PRI. Using airborne-based hyperspectral image, we found that PRI values were increased in shadow detection due to the reduction of high light induced physiological stress. However, the qualities of both PRI and NDVI data were dramatically decreased when the shadow index (SI) exceeded the threshold (SI<25). In addition, the PRI calculated using by 553.6 nm had best correlation with original PRI. This relationship was improved by multiple regression analysis including reflectances of RED and NIR. These results will be helpful to the understanding of physiological meaning on the application of PRI.

• Journal of Environmental Science International
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• v.33 no.7
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• pp.533-546
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• 2024

The coastal urchin barren phenomenon in our country began to spread and expand from the 1980s, centering on the southern coast and Jeju Island, and by the 1990s, it appeared along the east coast and nationwide. The urchin barren phenomenon is mainly conducted through field surveys by diving, but recently, various surveying techniques have been applied. In this study, a spectral library for terrestrial and marine areas was established for the identification of urchin barrens using airborne hyperspectral imagery, and the distribution area was analyzed through the SAM (spectral angle mapper) algorithm. An analysis of the urchin barren phenomenon in the five islands of the West Sea revealed that it occurrs in most areas, with the combined severity of the urchin barren phenomenon in Sapsido and Oeyeondo being approximately 19.9%. Hyperspectral imagery is expected to be highly useful not only for detecting the urchin barren phenomenon but also for managing and monitoring marine fishery resources through the classification of seaweeds.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.529-538
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• 2016

This study, multispectral and hyperspectral sensors were utilized to use radiometric cross validation for the purpose of radiometric quality evaluation of a 'KOMPSAT-3'. Images of EO-1 Hyperion and Landsat-8 OLI sensors taken in PICS site were used. 2 sections that have 2 different types of ground coverage respectively were selected as the site of cross validation based on aerial hyperspectral sensor and TOA Reflectance. As a result of comparison between the TOA reflectance figures of KOMPSAT-3, EO-1 Hyperion and CASI-1500, the difference was roughly 4%. It is considered that it satisfies the radiological quality standard when the difference of figure of reflectance in a comparison to the other satellites is found within 5%. The difference in Blue, Green, Red band was approximately 3% as a comparison result of TOA reflectance. However the figure was relatively low in NIR band in a comparison to Landsat-8. It is thought that the relatively low reflectance is because there is a difference of band passes in NIR band of 2 sensors and in a case of KOMPSAT-3 sensor, a section of 940nm, which shows the strong absorption through water vapor, is included in band pass resulting in comparatively low reflectance. To overcome these conditions, more detailed analysis with the application of rescale method as Spectral Bandwidth Adjustment Factor (SBAF) is required.