Abstract
Recently classification of remote sensing images using neural network approach is studied. However multitemporal LANDSAT image data is not used for classification. A problem in classification of remote sensing images is that we cannot get images in fixed wide area such as states of prefectures at one time because the range of sensors of artificial satellite is limited. For example, full area of Fukuoka prefecture is observed two separate images. For multitemporal images, several factors affect the spectrum at different observation dates. In this paper, we concentrate on the sunbeam factor from which we can estimate the intensity. Using the sun elevation angle from the data, we can estimate sunbeam intensity. We transform multitemporal images using radiance transformation which is based on path radiance model. We confirmed that radiance transformation is effective to the classification of multitemporal images from several experiments.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Place of Publication | Bellingham, WA, United States |
| Publisher | Society of Photo-Optical Instrumentation Engineers |
| Pages | 1668-1675 |
| Number of pages | 8 |
| Volume | 2501 |
| Edition | 3/- |
| ISBN (Print) | 0819418587 |
| Publication status | Published - 1995 |
| Externally published | Yes |
| Event | Visual Communications and Image Processing '95 - Taipei, Taiwan Duration: 1995 May 24 → 1995 May 26 |
Other
| Other | Visual Communications and Image Processing '95 |
|---|---|
| City | Taipei, Taiwan |
| Period | 95/5/24 → 95/5/26 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics
Cite this
Radiance transformation of multitemporal LANDSAT image for land cover classification. / Tanizaki, Fumihiro; Niimi, Michiharu; Kamata, Seiichiro; Kawaguchi, Eiji.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 2501 3/-. ed. Bellingham, WA, United States : Society of Photo-Optical Instrumentation Engineers, 1995. p. 1668-1675.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Radiance transformation of multitemporal LANDSAT image for land cover classification
AU - Tanizaki, Fumihiro
AU - Niimi, Michiharu
AU - Kamata, Seiichiro
AU - Kawaguchi, Eiji
PY - 1995
Y1 - 1995
N2 - Recently classification of remote sensing images using neural network approach is studied. However multitemporal LANDSAT image data is not used for classification. A problem in classification of remote sensing images is that we cannot get images in fixed wide area such as states of prefectures at one time because the range of sensors of artificial satellite is limited. For example, full area of Fukuoka prefecture is observed two separate images. For multitemporal images, several factors affect the spectrum at different observation dates. In this paper, we concentrate on the sunbeam factor from which we can estimate the intensity. Using the sun elevation angle from the data, we can estimate sunbeam intensity. We transform multitemporal images using radiance transformation which is based on path radiance model. We confirmed that radiance transformation is effective to the classification of multitemporal images from several experiments.
AB - Recently classification of remote sensing images using neural network approach is studied. However multitemporal LANDSAT image data is not used for classification. A problem in classification of remote sensing images is that we cannot get images in fixed wide area such as states of prefectures at one time because the range of sensors of artificial satellite is limited. For example, full area of Fukuoka prefecture is observed two separate images. For multitemporal images, several factors affect the spectrum at different observation dates. In this paper, we concentrate on the sunbeam factor from which we can estimate the intensity. Using the sun elevation angle from the data, we can estimate sunbeam intensity. We transform multitemporal images using radiance transformation which is based on path radiance model. We confirmed that radiance transformation is effective to the classification of multitemporal images from several experiments.
UR - http://www.scopus.com/inward/record.url?scp=0029238189&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029238189&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029238189
SN - 0819418587
VL - 2501
SP - 1668
EP - 1675
BT - Proceedings of SPIE - The International Society for Optical Engineering
PB - Society of Photo-Optical Instrumentation Engineers
CY - Bellingham, WA, United States
ER -