2015 Fiscal Year Final Research Report
Algorithm of semi-supervised classification for remotely sensed images with restricted training data
Project/Area Number |
25330199
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Perceptual information processing
|
Research Institution | Nagasaki University |
Principal Investigator |
KIYASU Senya 長崎大学, 工学研究科, 教授 (20234388)
|
Co-Investigator(Kenkyū-buntansha) |
SAKAI Tomoya 長崎大学, 工学研究科, 准教授 (30345003)
SONODA Kotaro 長崎大学, 工学研究科, 助教 (90415852)
|
Project Period (FY) |
2013-04-01 – 2016-03-31
|
Keywords | 半教師付き分類 / リモートセンシング / マルチスペクトル画像 |
Outline of Final Research Achievements |
We developed a method of semi-supervised classification for remotely sensed multispectral images to improve the accuracy of classification. The method includes expansion of training data based on clustering, classification considering the variation of spectral characteristics according to the location in the image, and classification considering the characteristics of spatial distribution of each category of objects in the image. We confirmed the validity of the method using remotely sensed data observed from satellites.
|
Free Research Field |
パターン情報処理工学
|