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Volume 24 (2004)
- Issue 4 Pages 357-
- Issue 3 Pages 255-
- Issue 2 Pages 133-
- Issue 1 Pages 1-
Volume 24, Issue 2
Displaying 1-9 of 9 articles from this issue
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Damdinsuren AMARSAIKHAN, Motoyuki SATO2004 Volume 24 Issue 2 Pages 133-139
Published: June 25, 2003
Released on J-STAGE: May 22, 2009
JOURNAL FREE ACCESSThe aim of this study is to demonstrate the validation of the original polarization components of Pi-SAR data for land cover mapping using a standard method. For this purpose, the original polarization and Pauli components of the Pi-SAR L-band data are used and the results are compared. As a method for the land cover discrimination, the traditional method of statistical maximum likelihood decision rule is selected. To increase the accuracy of the classification result, different spatial thresholds based on local knowledge are determined and used for the actual classification process. Moreover, to reduce the speckle noise and increase the spatial homogeneity of different classes of objects, a speckle suppression filter is applied to the original Pi-SAR data before applying the classification decision rule. Overall, the research indicated that the original Pi-SAR polarization components can be successfully used for separation of different land cover types without taking special polarization transformations.View full abstractDownload PDF (6476K) -
Shigeki KUZUOKA, Toshimi MIZUNO2004 Volume 24 Issue 2 Pages 140-152
Published: June 25, 2003
Released on J-STAGE: May 22, 2009
JOURNAL FREE ACCESSPSInSAR technique was applied to non-urbanized area in Japan to monitor the land deformation caused by earthquakes, volcanic activities, land slides, and so on. PSInSAR technique has been developed to monitor the land subsidence over urbanized area. In Japan, however, to monitor land deformations over non-urbanized area are often required. In this paper, after the principles of PSInSAR technique are explained, PSInSAR study for Tokai earthquake risk assessment is described. This was a joint research project for ESA. PSInSAR technique monitors the 2-dimensional distribution of land deformation as the time series. These results are useful to estimate the underground structure around Tokai area.
Before PSInSAR technique is applied to real projects, the feasibility of the study should be evaluated. Several conditions and limitations for PSInSAR were studied in this paper and two methods were proposed to evaluate the PS density-feasibility of PSInSAR. The PSInSAR feasibility of the area along Riv. Kakkonda near to Mt. Iwate was evaluated by the proposed two methods. PSInSAR study was then applied to the area and showed that the feasibility study was enough adequate. PSInSAR study around the area also showed the land deformation caused by an earthquake clearly and revealed the location of the active faults near the surface.View full abstractDownload PDF (15868K) -
Masayasu MAKI, Kazuo OKI, Hiroe TSUBAKI, Yoshifumi YASUOKA2004 Volume 24 Issue 2 Pages 153-162
Published: June 25, 2003
Released on J-STAGE: May 22, 2009
JOURNAL FREE ACCESSThe objective of this study was modeling of causal relationship between water quality and spectral reflectance. In this paper, linearity between water quality (chlorophyll-a and suspended solid) and spectral reflectance were firstly evaluated to choose non-linear transformation of the water quality variables. Next, the direct influences from each water quality to spectral reflectance ware evaluated by partial correlation analysis. Finally, the modeling of causal relationship between water quality and spectral reflectance was performed by using path diagram for Lake Kasumigaura with latent variables possibly corresponding to SS quality and quantity.View full abstractDownload PDF (2204K) -
Seitaro KIKUCHI, Mitsuyoshi TOMIYA2004 Volume 24 Issue 2 Pages 163-174
Published: June 25, 2003
Released on J-STAGE: May 22, 2009
JOURNAL FREE ACCESSA supervised classifier for satellite images by the Modified Counter-Propagation(MCP) is proposed. The MCP is a neural network for the competitive learning and it is the modified version of the Counter-Propagation, whose competitive layer is replaced by the Self-Organizing map(SOM). The Landsat image data are adopted as the input data of the MCP, and the output layer consists of the pixel values, which represent categories to be classified. Our result shows that the MCP can classify the data more accurately, objectively, and stably than the SOM only.View full abstractDownload PDF (14448K) -
A Supervised Classification Method Using a Three-Layered Self-Organizing Map and Counter PropagationMasafumi HOSOKAWA, Takashi HOSHI2004 Volume 24 Issue 2 Pages 175-181
Published: June 25, 2003
Released on J-STAGE: May 22, 2009
JOURNAL FREE ACCESSA supervised classification method using a self-organizing map (SOM) is proposed for the classification of remote sensing data. We adopt a three-layered SOM network and counter propagation learning method for multi-spectral data. The proposed classification method is employed to identify liquefied area in Kobe (Japan) that was damaged by the 1995 Hyogoken Nanbu earthquake, using SPOT XS data. The proposed method provides a category map to visualized the SPOT XS data and offers higher classification accuracy than either the maximum likelihood or back-propagation methods (BP).View full abstractDownload PDF (4318K) -
Chinatsu YONEZAWA2004 Volume 24 Issue 2 Pages 182-184
Published: June 25, 2003
Released on J-STAGE: May 22, 2009
JOURNAL FREE ACCESSDownload PDF (2359K) -
[in Japanese]2004 Volume 24 Issue 2 Pages 185-190
Published: June 25, 2003
Released on J-STAGE: May 22, 2009
JOURNAL FREE ACCESSDownload PDF (6604K) -
2004 Volume 24 Issue 2 Pages 201-204
Published: June 25, 2003
Released on J-STAGE: May 22, 2009
JOURNAL FREE ACCESSDownload PDF (7040K) -
2004 Volume 24 Issue 2 Pages 214-216
Published: June 25, 2003
Released on J-STAGE: May 22, 2009
JOURNAL FREE ACCESSDownload PDF (1851K)
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