• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.425-427
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• 2004

Motivation of this study is based on these two aspects: geologic uses of ASTER and application scheme of Spectral Mixture Analysis. This study aims at geologic mapping for mineral exploration using ASTER and LANDSAT 7 ETM+ at Mongolian plateau region by SMA. After basic pre-processing such as the normalization, geometric corrections and calibration of reflectance, related to endmembers selection and spectral signature deviation, both methods using spectral library and using PPI(Pixel Purity Index) are performed and compared on a given task. Based on these schemes, SMA is performed using LANDSAT 7 ETM+ and ASTER image. As the results, fraction map showing geologic rock types are enough to meet purposes such as geologic mapping and mineral potential mapping in the case of both uses of these different types of remotely sensed images. It concluded that this approach based on SMA with LANDSAT and ASTER is regarded as one of effective schemes for geologic remote sensing.

• Proceedings of the KSRS Conference
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• v.2
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• pp.848-851
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• 2006

Green vegetation is one of the most critical factors for environment conditions thorough modulating evapotranspiration and absorption of solar radiation. Thus, fractional green vegetation cover (FVC) plays an important role in observing and managing environment. Remote sensing provides a seemingly obvious data source for quantifying FVC over large area. Therefore we compared a set of methods for estimating FVC using hyperspectral remote sensing data. For our study, we used Hyperion imagery acquired in April, 2002. In order to achieve our efforts, we analyzed simple NDVI-based method and spectral mixture analysis (SMA) models that were applied a variety of combinations of possible endmembers.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.11a
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• pp.23-29
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• 2003

Detection in remotely sensed images can be conducted spatially, spectrally or both [2]. If the images have high spatial resolution, materials can be detected by using spatial and spectral information, unless we can't see the object embedded in a pixel. In this paper, we intend to solve the limit of spatial resolution by using the hyperspectral image which has high spectral resolution. Therefore, the Linear Spectral Mixing(LSM) Model which is sub-pixel detection algorithm is used to solve this problem. To find class Endmembers, we applied Geometric Model with MNF(Minimum Noise Fraction) transformation. From the result of sub-pixel detection algorithm, we can see the detection of water is satisfied and the object shape cannot be extracted but the possibility of material existence can be identified.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.161-166
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• 2003

Hyperspectral imagery have high spectral resolution and provide the potential for more accurate and detailed information extraction than any other type of remotely sensed data. In this paper, the "Linear Spectral Unmixing" model which is one solution to overcome the limit of spatial resolution for remote sensing data was introduced and we applied the algorithm to hyperspectral image. The result was not good because of some problems such as image calibration and used endmembers. Therefore, we analyzed the cause and had a search for a solution.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.732-734
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• 2003

In the middle -resolution remote sensing, the Ground Sampled Distance(GSD) sensed and sampled by the detector is generally larger than the size of objects(or materials) of interest, in which case several objects are embedded in a single pixel and cannot be detected spatially. This study is intended to solve this problem of a hyperspectral data with high spectral resolution. We examined the detection algorithm, Linear Spectral Mixing Model, and also made a test on the Hyperion data. To find class Endmembers, we applied two methods, Spectral Library and Geometric Model, and compared them with each other.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.334-336
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• 2003

Satellite and airborne hyperspectral sensor images are suitable for investigating the vegetation state in agricultural land. However, image data obtained by an optical sensor inevitably includes mixels caused by high altitude observation. Therefore, mixel analysis method, which estimates both the pure spectra and the coverage of endmembers simultaneously, is required in order to distinguish the qualitative spectral changes due to the chlorophyll quantity or crop variety, from the quantitative coverage change. In this paper, we apply our agricultural independent component analysis (ICA) model to an airborne hyperspectral sensor image, which includes noise and fluctuation of coverage, and estimate pure spectra and the mixture ratio of crop and soil in agricultural land simultaneously.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.361-370
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• 2015

Various algorithms such as Chronochrome(CC), Principle Component Analysis(PCA), and spectral unmixing have been studied for hyperspectral change detection. Change detection by spectral unmixing offers useful information on the nature of the change compared to the other change detection methods which provide only the locations of changes in the scene. However, hyperspectral change detection by spectral unmixing is still in an early stage. This research proposed a new approach to extract endmembers, which have identical properties in temporally different images, by Iterative Error Analysis (IEA) and Spectral Angle Mapper(SAM). The change map obtained from the difference of abundance efficiently showed the changed pixels. Simulated images generated from Compact Airborne Spectrographic Imager (CASI) and Hyperion were used for change detection, and the experimental results showed that the proposed method performed better than CC, PCA, and spectral unmixing using N-FINDR. The proposed method has the advantage of automatically extracting endmembers without prior information, and it could be applicable for the real images composed of many materials.

• Proceedings of the KSRS Conference
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• v.1
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• pp.297-300
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• 2006

The knowledge of fuel load and composition is important for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential of reduction the uncertainty in mapping fuels and offers the best approach for improving our abilities. This paper compared the results of object-oriented classification to a pixel-based classification for fuel type map derived from Hyperion hyperspectral data that could be enable to provide this information and allow a differentiation of material due to their typical spectra. Our methodological approach for fuel type map is characterized by the result of the spectral mixture analysis (SMA) that can used to model the spectral variability in multi- or hyperspectral images and to relate the results to the physical abundance of surface constitutes represented by the spectral endmembers. Object-oriented approach was based on segment based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery

• Korean Journal of Geomatics
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• v.4 no.1
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• pp.1-8
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• 2004

In the middle-resolution remote sensing, the Ground Sampled Distance (GSD) that the detector senses and samples is generally larger than the actual size of the objects (or materials) of interest, and so several objects are embedded in a single pixel. In this case, as it is impossible to detect these objects by the conventional spatial-based image processing techniques, it has to be carried out at sub-pixel level through spectral properties. In this paper, we explain the sub-pixel analysis algorithm, also known as the Linear Spectral Mixing (LSM) model, which has been experimented using the Hyperion data. To find Endmembers used as the prior knowledge for LSM model, we applied the concept of the convex geometry on the two-dimensional scatter plot. The Atmospheric Correction and Minimum Noise Fraction techniques are presented for the pre-processing of Hyperion data. As LSM model is the simplest approach in sub-pixel analysis, the results of our experiment is not good. But we intend to say that the sub-pixel analysis shows much more information in comparison with the image classification.

• Journal of Environmental Science International
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• v.23 no.6
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• pp.1049-1060
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• 2014

We investigated the spatiotemporal variations of dissolved inorganic nutrients along a saline gradient to estimate nutrient fluxes in the Seomjin River estuary during dry (March 2005, March 2006, March 2007, and March 2008) and rainy seasons (August 2005, July 2006, July 2007, and July 2008). Dissolved inorganic nitrogen concentrations were similar in the endmembers of freshwater for the rainy and dry seasons. In contrast, the concentrations of dissolved inorganic phosphate and silicate in the rainy season were approximately 2-3 times higher than those in the dry season. River discharge was approximately 10 times higher in the rainy season ($212m^3sec^{-1}$) than in the dry season ($21m^3sec^{-1}$). The fluxes of dissolved inorganic nitrogen, phosphate, and silicate were 2.91, 0.004, and 2.51 tons $day^{-1}$ in the dry season and 7.45, 0.421, and 30.5 tons $day^{-1}$ in the rainy season, respectively. Although the range of nutrient concentrations were similar to previous results from investigations in the Seomjin River estuary, the nutrient fluxes were differed according to river discharge for different survey periods.