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

• Korean Journal of Remote Sensing
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• v.14 no.4
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• pp.343-352
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• 1998

Spectral reflectance data 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. For corn discrimination from two other legumes, peanut and soybean, June 22 among measurements dates was the best since 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 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
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• 2002.10a
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• pp.208-208
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• 2002

Relationship between chlorophyll a in the East China Sea and spectral bands (412, 443,490, (510), 555, (676,765) in) of OSMI (Ocean Scanning Multi-Spectral Imager) including the profile multi-spectral radiometer (PRR-800) was studied. The values of remote sensing reflectance (Rrs) at the bands corresponding to the field chlorophyll a in α in the East China Sea were much higher than those in clear waters off California, USA. In case of the particle absorptions related to the chlorophyll a concentration at the spectral bands (440, 670 nm) were much higher in the East China Sea than the ones in the clean waters off California. The normalized water leaving radiances (nLw) at 412, 443, 490, 555 m of OSMI and field chlorophyll a in the East China Sea were correlated each other. According to the results, the relationship between field chlorophyll a and nLw 410 m in OSMI bands was the lowest, whereas that between the field chlorophyll a and nLw 555 nm in the bands was the highest. Reciprocal action between the field chlorophyll a and the band ratio of the OSMI bands (nLw410/nLw555, nLw443/nLw555, nLw490/nLw555) was also studied. Correlation between the chlorophyll a and the band ratio (nLw490/nLw555) was highest in the OSMI bands. Relationship between the chlorophyll a and the ratio (nLw443/nLw555) was higher than one in the nLw410/nLw555. The difference in the estimated chlorophyll α (mg/m3) between OSMI and SeaWiFS (Sea Viewing Wide Field-of-View Sensor) at the special observing stations in the northern eastern sea of Jeju Island in february 25, 2002 was about less than 0.3 mg/m3 within 3 hours. It is suggested that OC2 (ocean color chlorophyll 2 algorithm) be used to get much better estimation of chlorophyll α from OSMI than the ones from the updated algorithms as OC4.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.396-402
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• 1999

The aim of this study is to assess the OSMI (Ocean Scanning Multi-spectral Imager), whose central bands are 443nm, 490nm, 510nm, 555nm, 670nm, and 865nm, for agricultural applications. Radiance measurements, used to determine per cent reflectance of canopies and soils, were acquired with spectro-radiometers (Li-1800;330~1,100nm, GER-SFOV;350 ~2,500nm, and MSR-7000; 300~2,500nm) in situ for crops and indoors for soils. OSMI equivalent bands and their ratio values were prepared(20nm interval for bands 1~5; 4nm interval for band 6) by averaging spectral reflectance values to the real OSMI bands and analyzed as to crop growth parameters, leaf area index (LAI), total dry matter, and growth index in crops and physiochemical properties in soils. Spectral variations for each growth stage in rice and for crop discrimination in upland crops were significant statistically. In soils, clay and water content, CEC (Cation Exchange Capacity), free iron oxide, and some cation content were correlated with the OSMI equivalent bands. The result of this study shows OSMI wave bands would be promising for agricultural application in terms of spectral information and resolution.

• Korean Journal of Remote Sensing
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• v.15 no.4
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• pp.307-319
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• 1999

The aim of this study is to assess the OSMI (Ocean Scanning Multi-spectral Imager), whose central bands are 443nm, 490nm, 510nm, 555nm, 670nm, and 865nm, for agricultural applications. Radiance measurements, used to determine per cent reflectance of canopies and soils, were acquired with spectro-radiometers (Li-1800;330∼1,100nm, GER-SFOV;350∼2,500nm, and MSR-7000; 300∼2,500nm) in situ for crops and indoors for soils. OSMI equivalent bands and their ratio values were prepared(20nm interval for bands 1∼5; 40nm interval for band 6) by averaging spectral reflectance values to the real OSMI bands and analyzed as to crop growth parameters, leaf area index (LAI), total dry matter, and growth index in crops and physiochemical properties in soils. Spectral variations for each growth stage in rice and for crop discrimination in upland crops were significant statistically. In soils, clay and water content, CEC (Cation Exchange Capacity), free iron oxide, and some cation content were correlated with the OSMI equivalent bands. The result of this study shows OSMI wave bands would be promising for agricultural application in terms of spectral information and resolution.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3E
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• pp.131-139
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• 2010

This paper proposes a novel spectral hole substitution technique for low bit-rate audio coding. The spectral holes frequently occurring in relatively weak energy bands due to zero bit quantization result in severe quality degradation, especially for harmonic signals such as speech vowels. The enhanced aacPlus (EAAC) audio codec artificially adjusts the minimum signal-to-mask ratio (SMR) to reduce the number of spectral holes, but it still produces noisy sound. The proposed method selectively predicts the spectral shapes of hole bands using either intra-band correlation, i.e. harmonically related coefficients nearby or inter-band correlation, i.e. previous frames. For the bands that have low prediction gain, only the energy term is quantized and spectral shapes are replaced by pseudo random values in the decoding stage. To minimize perceptual distortion caused by spectral mismatching, the criterion of the just noticeable level difference (JNLD) and spectral similarity between original and predicted shapes are adopted for quantizing the energy term. Simulation results show that the proposed method implemented into the EAAC baseline coder significantly improves speech quality at low bit-rates while keeping equivalent quality for mixed and music contents.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.16-28
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• 2013

Recently the various applications and spectral indices development of airborne hyperspectral imagery(A-HSI) has been increased. Especially the vegetation indices (VIs) were used to verify stress and vigor of vegetation. The VIs needs two or more spectral bands selectively to calculate as NIR(near infrared) and red wavelength. The A-HIS has specific band characteristics as narrow, continues and many. The A-HIS has narrow, continues and many specific band characteristics. That could be make it confuse which of bands could be explained for appropriate vegetation characteristics. If the A-HIS bands is not the same the wavelength with VIs' development band setting, then it need a selection adequate for spectral characteristics of target vegetation. Therefore we set 4 substitute bands for NIR and red wavelength respectively and calculated two VIs combined with substitute bands such as NDVI(normalized difference vegetation index) and MSRI(modified simple ratio index). To consider the variation of each VIs, we adapted the image differencing method of change detection technique. Also, we used spectral derivative to identify appropriate bands for spectral characteristics of digital forest cover type map. The result of adequate bands for two VIs selected red #3 as 680.2nm and NIR #2 as 801.7nm. This wavelength was good for any forest type in low variations.

• Korean Journal of Remote Sensing
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• v.6 no.2
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• pp.115-133
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• 1990

Study was made on the spectral characteristics of rock samples including bentonites collected from the northern Ulsan area. The geology of the area consists mainly of sediments of the Kyongsang Series and Bulguksa granite, the Tertiary volcanics, andesites and tuffs. Relative reflectances of meshed samples(2.5~10mm) to BaSO$_4$ are measured at 6 Landsat TM spectral windows (excluding the thermal band) with HHRR, and their reflection charactristics were analysed. In addition, three different data selection schemes including the Eulidean distance, multiple regression, and PCA weight methods were applied to the 30 TM ratio channels, derived from the above 6 bands. The selected data sets were subject to two unsupervised classification techniques(FA and ISODATA) in order to compare the effectiveness for classification of particularly bentonite from others. As a result, in ISODATA analysis the multiple regression model shows the best, followed by the Euliean distances one. The PCA weight model seems to show some confusion. In FA, though difficult for quantitative analysis, the best still seems to be the regression model. Among ratio bands, rations of band 7 or 5 against other bands represent the best contribution in classification of bentonites from others.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.63-87
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• 1985

Considerable experience has been reported on the use of spectral data to measure the canopy biomass of dryland grain crops and the use of these estimates to forecast subsequent grain yield. These basic procedures were retested to assess the use of the general process to forecasting grain yield for paddy rice. The use of the ratio of a multiband radiometer simulation of Thematic Mapper band 4(.76 to .90 .mu.m) divided by band 3 (.63 to .69 .mu.m) was tested to estimate the canopy biomass of paddy rice as a function of the stage of development of the rice. The correlation was found to be greatest (R = .94) at panicle differentiation about midway through the development cycle of the rice canopy. The use of this ratio of two spectral bands as a surrogate for canopy biomass was then tested for its correlation against final grain yield. These spectral estimates of canopy biomass produced the highest correlations with final grain yield (R = .87) when measured at the canopy development stages of panicle differentiation and heading. The impact of varying the amounts of supplemental nitrogen on the use of spectral measuremants of canopy biomass to estimate grain yield was also determined. The effect of the development of a significant amount of weed biomass in the rice canopy was also clearly detected.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.55-65
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• 2020

Image classification needs the spectral similarity comparison between spectral features of each pixel and the representative spectral features of each class. The spectral similarity is obtained by computing the spectral feature vector distance between the pixel and the class. Each spectral feature contributes differently in the image classification depending on the class separability of the spectral feature, which is computed using a suitable vector distance measure such as the Bhattacharyya distance. We propose a method to determine the weight value of each spectral feature in the computation of feature vector distance for the similarity measurement. The weight value is determined by the ratio between each feature separability value to the total separability values of all the spectral features. We created ten spectral features consisting of seven bands of Landsat-8 OLI image and three indices, NDVI, NDWI and NDBI. For three experimental test sites, we obtained the overall accuracies between 95.0% and 97.5% and the kappa coefficients between 90.43% and 94.47%.