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Volume 38 (1999)
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Volume 38, Issue 6
Displaying 1-9 of 9 articles from this issue
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[in Japanese]1999 Volume 38 Issue 6 Pages 1
Published: January 05, 2000
Released on J-STAGE: March 19, 2010
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[in Japanese]1999 Volume 38 Issue 6 Pages 2-3
Published: January 05, 2000
Released on J-STAGE: March 19, 2010
JOURNAL FREE ACCESSDownload PDF (2165K) -
[in Japanese], [in Japanese]1999 Volume 38 Issue 6 Pages 4-5
Published: January 05, 2000
Released on J-STAGE: March 19, 2010
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Akira OTUKA, Ken TANIWAKI1999 Volume 38 Issue 6 Pages 6-13
Published: January 05, 2000
Released on J-STAGE: March 19, 2010
JOURNAL FREE ACCESSA new method of extracting broad-leaf weeds from grassland, Variance Extraction Method (VEM), has been proposed. The method applies texture analysis to weed extraction. Statistical variance was found suitable as a feature parameter of the texture analysis. Only weeds can be extracted because variance calculated on the broad leaves of the weeds is small and it is large in the grassland region. In this paper, characteristics of the proposed method are closely investigated and a conditional equation of extraction is presented for the design of weed system.View full abstractDownload PDF (11102K) -
Masafumi HOSOKAWA, Yosuke ITO, Takashi HOSHI1999 Volume 38 Issue 6 Pages 14-23
Published: January 05, 2000
Released on J-STAGE: March 19, 2010
JOURNAL FREE ACCESSA supervised classification method using a self-organizing map (SOM) is proposed to classify remote sensing data. SOM has a characteristic that a probability density function of input data is represented as a feature map. The proposed method is realized by creating a category map from the feature map of SOM. The category map can visualize characteristics of SPOT HRV data and it is also employed as a supervised classification method. The proposed method extracts liquefied area in Kobe (Japan) damaged by the 1995 Hyogoken Nanbu earthquake using the SPOT HRV data and the category map. As an experimental result, it is shown that classification accuracies of the proposed method are higher than those of the maximum likelihood and the back-propagation methods.View full abstractDownload PDF (8077K) -
Ryota NAGASAWA1999 Volume 38 Issue 6 Pages 24-33
Published: January 05, 2000
Released on J-STAGE: March 19, 2010
JOURNAL FREE ACCESSThe Yao River basin, located in the Nan Province, north Thailand, is one of the most critical watersheds in terms of soil erosion. USLE method was combined with GIS technology so as to estimate the soil erosion volume in this basin. As a result, the total amount of 131, 500 ton/year for the whole basin was calculated. Based on the spatial distribution of USLE value, the most hazardous sub basin with 20, 075 ton/year was detected on the left side of the lower Yao River. Then, USLE analysis at larger scale was conducted for this sub-basin. By applying the higher resolution DEM and aerial photo interpretation, it became clear that the actual value was 8, 940 ton/year because the slopes were less steep and more vegetated. Although there is no land conservation practice, the supposed three conditions (contouring, stripping and terracing) were simulated in order to evaluate the effectiveness of each type of practice. The result shows that the erosion volume was reduced to less than half by constructing the stripping or terracing practice.View full abstractDownload PDF (9747K) -
Kohei ARAI, Yasunori TERAYAMA1999 Volume 38 Issue 6 Pages 34-40
Published: January 05, 2000
Released on J-STAGE: March 19, 2010
JOURNAL FREE ACCESSA field campaign for various calibration of ADEOS/AVNIR and OCTS was conducted at Ivanpah Playa, California, USA on March 4 1997. Through a careful analysis of the observed optical depth of the atmosphere, the measured surface Bidirectional Reflectance Distribution Function (BRDF) and the other meteorological data, Top of Atmosphere (TOA) radiance was estimated based on the Radiative Transfer Equation (RTE) and was compared to the real AVNIR and OCTS derived radiance. It is found that the difference between both ranges from around -3.5 to -20% for AVNIR while those for OCTS ranges from approximately -9.5 to -15%.View full abstractDownload PDF (4319K) -
Muhamad SADLY, Yoshizumi YASUDA1999 Volume 38 Issue 6 Pages 41-51
Published: January 05, 2000
Released on J-STAGE: March 19, 2010
JOURNAL FREE ACCESSThe main topic of this paper is the use of the self built learning control mechanism in backpropagation training, under the conditions encountered in processing remote sensing data. The approach based on the self-built learning control mechanism of controlling the learning rates and tuning of momentum term at the same time automatically in training of the feedforward neural network. An important feature of our novel method is that the initial learning parameters are not crucial to the success of the training, because the learning parameters are controlled that does not cause instability. Furthermore, the automatic initials learning rate selection and the optimal values of the learning control parameters were experimentally discussed. The proposed method was verified by a land cover study over Cianjur area, West-Java, Indonesia, with MOS-1 multi-spectral imagery. The simulations were presented to indicate the remarkable advantages of the proposed approach in both convergence rate and time saving.View full abstractDownload PDF (4788K) -
[in Japanese]1999 Volume 38 Issue 6 Pages 52-53
Published: January 05, 2000
Released on J-STAGE: March 19, 2010
JOURNAL FREE ACCESSDownload PDF (2126K)
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