Details

Autor(en) / Beteiligte

• Ivits, Eva
• Lamb, Alistair
• Langar, Filip
• Hemphill, Scott
• Koch, Barbara

Titel

Orthogonal Transformation of Segmented SPOT5 Images

Ist Teil von

• Photogrammetric engineering and remote sensing, 2008-11, Vol.74 (11), p.1351-1364

Ort / Verlag

American Society for Photogrammetry and Remote Sensing

Erscheinungsjahr

2008

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• Brightness, Greenness, and Wetness Tasselled Cap parameters were derived for the SPOT5 sensor. Their robustness through space and time and their discrimination power in land-cover classes was investigated. Four images were acquired from March and September 2003, and in July and November 2004 over Germany. A fifth SPOT5 image was acquired from Cameroon, West Africa in January 2003. The Tasselled Cap parameters were extracted with the Gram-Schmidt orthogonalization technique for each image independently. One set of combined parameters was created for Germany using samples from the four SPOT5 images simultaneously. Each SPOT5 image was transformed into Brightness, Greenness, and Wetness with their own with the combined and the July parameters. Spearman's Rho correlation analysis was carried out between the Tasselled Cap counterparts acquired with the various parameters. Brightness exhibited nearly perfect correlations between the images in Germany; in Cameroon however, the images were inconsistent. Greenness and Wetness displayed a difference of up to 35 percent in November in Germany. The Wetness counterparts in Cameroon exhibited a 7 percent difference. Canonical discrimination analysis revealed that the components from July had the highest discrimination power and that Greenness expressed the highest association to the first canonical axis in all images. In March, July, and November, Brightness was the second most important Tasselled Cap component, in September the Wetness and in Cameroon the Greenness. These results indicate that the Tasselled Cap components are not stable between different seasons and geographical locations. They can be successfully used for land-cover discrimination if the images are transformed with parameters appropriate to the investigated season respective biogeographical zone.