3 0 0 0 三次元立体ヒストグラムを利用した極座標表示によるカテゴリー分類
- 著者
- 石黒 洋行 富谷 光良
- 雑誌
- 学術講演会論文集 = Proceedings of the ... Japanese Conference on Remote Sensing (ISSN:09163611)
- 巻号頁・発行日
- vol.20, pp.93-94, 1996-05-01
- 参考文献数
- 4
3 0 0 0 OA 三次元グラフイックスによる三次元立体ヒストグラムのリモートセンシング多重分光画像の解析
- 著者
- 富谷 光良 石黒 洋行 中島 康裕 豊田 弘道
- 出版者
- The Remote Sensing Society of Japan
- 雑誌
- 日本リモートセンシング学会誌 (ISSN:02897911)
- 巻号頁・発行日
- vol.15, no.3, pp.232-241, 1995-06-30 (Released:2009-05-22)
- 参考文献数
- 9
The computer graphical method for analysis of remote sensing multi-spectral image data is proposed. Three-dimensional histogram is constructed in three-dimensional feature space from the image data and represented by three-dimensional computer graphics. On the histogram fine structure of distribution of data in the feature space can be observed. Therefore the categories of remote sensing multi-spectral image data can be identified easily on the computer screen. Each distribution of water, vegetation and soil etc. has its charasteristic features in the histogram. One of the most distinguished features is that distribution of data of each category is long and narrow, and the principle component axes of the categories concentrate on a point near the origin of the feature space.
- 著者
- 富谷 光良 石黒 洋行 中島 康裕 山口 基貴 豊田 弘道
- 雑誌
- 日本リモートセンシング学会誌 = Journal of the Remote Sensing Society of Japan (ISSN:02897911)
- 巻号頁・発行日
- vol.15, no.4, pp.326-337, 1995-09-30
- 参考文献数
- 8
- 被引用文献数
- 3
2 0 0 0 OA リモートセンシング多重分光画像における線形代数演算によるカテゴリー抽出について
- 著者
- 富谷 光良 石黒 洋行 中島 康裕 山口 基貴 豊田 弘道
- 出版者
- The Remote Sensing Society of Japan
- 雑誌
- 日本リモートセンシング学会誌 (ISSN:02897911)
- 巻号頁・発行日
- vol.15, no.4, pp.326-337, 1995-09-30 (Released:2009-05-22)
- 参考文献数
- 8
The linear algebraic analysis of remotely sensed multispectral image is studied. The inner product is applied to the classification and the outer product to systematic treatment of mixels. The analysis uses essentially angular distribution of data with respect to the origin of feature space. The offsets which are involved in the data are estimated by the 3-dimensional histogram and subtracted from the data. Then the fact that pixels have the same angle in the feature space strongly implies that they have the same reflectances and belong to the same category. Therefore the angles of data is the most important information to classify the categories.