• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1055-1057
• /
• 2003

In this research, the concrete with paint film was classified using hyper-spectral remote sensing. First, spectral characteristics of concrete and concrete with some kinds of paint films were investigated with a spectrometer. Second, using reflectance and first order derivative, spectral characteristics of the normal concrete and the concrete with paint film were classified. By using hyper-spectral remote sensing, not only extraction of crack but also inspection of paint film distribution is possible.

• Korean Journal of Remote Sensing
• /
• v.25 no.3
• /
• pp.233-242
• /
• 2009

This paper presents two different types of supervised classifiers such as support vector machine (SVM) and spectral angle mapper (SAM). The Compact Airborne Spectrographic Imager (CASI) high resolution aerial image was classified with the above two classifier. The image was classified into eight land use /land cover classes. Accuracy assessment and Kappa statistics were estimated for SVM and SAM separately. The overall classification accuracy and Kappa statistics value of the SAM were 69.0% and 0.62 respectively, which were higher than those of SVM (62.5%, 0.54).

• Korean Journal of Remote Sensing
• /
• v.26 no.6
• /
• pp.693-703
• /
• 2010

High spectral resolution of hyperspectral data enables analysis of complex natural phenomena that is reflected on the data nonlinearly. Although many manifold learning methods have been developed for such problems, most methods do not consider the spatial correlation between samples that is inherent and useful in remote sensing data. We propose a manifold learning method which directly combines the spatial proximity and the spectral similarity through kernel PCA framework. A gain factor caused by spatial proximity is first modelled with a heat kernel, and is added to the original similarity computed from the spectral values of a pair of samples. Parameters are tuned with intelligent grid search (IGS) method for the derived manifold coordinates to achieve optimal classification accuracies. Of particular interest is its performance with small training size, because labelled samples are usually scarce due to its high acquisition cost. The proposed spatial kernel PCA (KPCA) is compared with PCA in terms of classification accuracy with the nearest-neighbourhood classification method.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1400-1402
• /
• 2003

The objective of this paper is to determine the suitable spectral bands for monitoring growth status change during a long period. The long-term ground-level reflectance spectra as well as LAI and biomass were obtained in xiaotangshan area, Beijing, 2001. The narrow-band NDVI type spectral indices by all possible two bands were calculated their correlation coefficients R$^2$ with biomass and LAI. The best NDVIs must have higher R$^2$ with both biomass and LAI. The reasonable band centers and band widths were determined by a systematically increasing bandwidth centered over a wavelength. In addition, the first 19 bands of MODIS were simulated and investigated. Each developed spectral indices was then validated by the biomass and LAI time series using the generalized vector angle. It turned out that six new NDVI type indices within 750-1400nm were developed. NDVI(811_10,957_10) and NDVI(962_10,802_10) performed best. No satisfactory conventional NDVI formed by red and NIR bands were found effective. MODIS_NDVI(band19, band17) and MODIS_NDVI(band19, band2) were much better than MODIS_NDVI(band2,band1) for growth monitoring.

• Korean Journal of Remote Sensing
• /
• v.23 no.1
• /
• pp.21-32
• /
• 2007

Fire fuel map is one of the most critical factors 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 to reduce the uncertainty in mapping fuels and offers the best approach for improving our abilities. Especially, Hyperspectral sensor have a great potential for mapping vegetation properties because of their high spectral resolution. The objective of this paper is to evaluate the potential of mapping fuel properties using Hyperion hyperspectral remote sensing data acquired in April, 2002. Fuel properties are divided into four broad categories: 1) fuel moisture, 2) fuel green live biomass, 3) fuel condition and 4) fuel types. Fuel moisture and fuel green biomass were assessed using canopy moisture, derived from the expression of liquid water in the reflectance spectrum of plants. Fuel condition was assessed using endmember fractions from spectral mixture analysis (SMA). Fuel types were classified by fuel models based on the results of SMA. Although Hyperion imagery included a lot of sensor noise and poor performance in liquid water band, the overall results showed that Hyperion imagery have good potential for wildfire fuel mapping.

• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.565-565
• /
• 2004

• Korean Journal of Remote Sensing
• /
• v.21 no.4
• /
• pp.341-369
• /
• 2005

Hyperspectral images have emerged as a new and promising remote sensing data that can overcome the limitations of existing optical image data. This study was designed to provide a comprehensive review on definition, data processing methods, and applications of hyperspectral data. Various types of airborne, spaceborne, and field hyperspectral image sensors were surveyed from the available literatures and internet search. To understand the current status of hyperspectral remote sensing technology and research development, we collected several hundreds research papers from international journals (IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, Remote Sensing of Environment and AVIRIS Workshop Proceedings), and categorized them by sensor types, data processing techniques, and applications. Although several hyperspectral sensors have been developing, AVIRIS has been a primary data source that the most hyperspectral remote sensing researches were relied on. Since hyperspectral data have very large data volume with many spectral bands, several data processing techniques that are particularly oriented to hyperspectral data have been developed. Although atmospheric correction, spectral mixture analysis, and spectral feature extraction are among those processing techniques, they are still in experimental stage and need further refinement until the fully operational adaptation. Geology and mineral exploration were major application in early stage of hyperspectral sensing because of the distinct spectral features of rock and minerals that could be easily observed with hyperspectral data. The applications of hyperspectral sensing have been expanding to vegetation, water resources, and military areas where the multispectral sensing was not very effective to extract necessary information.

• Proceedings of the KSRS Conference
• /
• 1998.09a
• /
• pp.370-375
• /
• 1998

Spectral reflectance signatures of upland crops at OSMI bands were collected and evaluated for the feasibility of crop discrimination knowledge-based on crop calendar. Effective bands and their ratio values for discriminating corn from two other legumes were defined with OSMI equivalent bands and their ratio values. June 22 among measurements dates was the best date for corn discrimination from two other legumes, peanut and soybean, because all OSMI equivalent bands and their ratio values in June 22 were highly significant for corn separability. Phenological growth stage of a silage corn (rs510) could be estimated as a function of spectral reflectance signatures in vegetative stage. Five growth stage prediction models were generated by the SAS procedures REG and STEPWISE with OSMI equivalent bands and their ratio values in vegetative stage.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.573-575
• /
• 2003

In satellite spectra, Though the magnitude varies with intensity of sunstroke, dip angle of land so on, the shape is less deformed with these effects. from this point of view, we have developed a spectral shape-dependent analysis utilizing a normalization procedure by the spectral integral and applied it to Landsat/TM spectra. Inevitable topographic and atmospheric effects can be suppressed. The correction algorithm is very simple and timesaving and the suppression of topographic effects is especially effective. Normalized band 4 is almost linear to NDVI values, and is available to the vegetation index.

• Agricultural and Biosystems Engineering
• /
• v.7 no.1
• /
• pp.27-41
• /
• 2006

Assessment of nitrogen and chlorophyll content from crop leaves can help growers adjust N fertilizer rates to meet the demands of the crop. Numerous researchers have presented their studies about spectral signature of plant leaves to characterize the plant features. However, interrelational review and summary were limited and a communication gap exists between the plant science and optical engineering. Understanding the mechanism of leaf interaction to electromagnetic radiation and factors affecting spectrophotometric measurements can enhance the foundation of optical remote sensing technologies. This paper provides extensive review of previous works in optical sensing and explains the basics of plant optics, spectral measurements for plant stress, factors that affect sensitivity to spectral analysis, and applications that deploy optical remote sensing technologies.