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Semelhante a URBAN AREA PRODUCT SIMULATION FOR THE ENMAP HYPERSPECTRAL SENSOR.ppt
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URBAN AREA PRODUCT SIMULATION FOR THE ENMAP HYPERSPECTRAL SENSOR.ppt
- 1. URBAN AREA PRODUCT SIMULATION FOR THE ENMAP HYPERSPECTRAL SENSOR P.Gamba, A. Villa, A. Plaza, J. Chanussot, J. A. Benediktsson
- 15. EXPERIMENTS Reference data SVM SVM-SU ROSIS Center (3x downscale) OA (%) 98.11 79.56 81.89 Δ (%) - 19.55 - 16.22 ROSIS Center (5x downscale) OA (%) 98.11 70.97 74.32 Δ (%) - 27.14 - 23.79
- 16. THEMATIC MAPS SVM on original HR data ( ground truth ) SVM on LR data 70.97% Finer Classification 74.32%
- 17. THEMATIC MAPS SVM on original HR data ( ground truth ) SVM on LR data 70.97% Finer Classification 74.32%
Notas do Editor
- 1) All pixels are classified with SVM. If the probability to belong to a class is greater that a chosen treshold, the pixel is considered pure and labeled
- 2) Spectral unmixing is applied to mixed pixels to determined each class abundance. According to the desired zoom factor, each pixel is split into a number of sub-pixels. Each sub-pixel is assigned to a class according to its abundace
- 3) Final spatial regularization (by Simulated Annealing)
- The overall accuracy of the Reference data is the OA obtained by classifying the high spatial resolution data set with an SVM and 100 samples per class.
- The ellipses show that the method SVM-SU improves the classification accuracy. However, some information is definitely lost, as shown by the arrow: the red class (corresponding to the shadow) can not be found in the final map, close to the yellow class.