URBAN AREA PRODUCT SIMULATION FOR THE ENMAP HYPERSPECTRAL SENSOR.ppt
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1) The document discusses the potential of hyperspectral sensors like EnMAP for urban monitoring tasks, which is challenging due to mixed pixels from heterogeneous land covers. 2) It creates realistic EnMAP images by degrading the spatial resolution of urban scene images and applies classification methods. 3) The results show that while classification accuracy decreases significantly with lower resolution, spectral unmixing techniques can improve accuracy by exploiting sub-pixel information. However, the utility of hyperspectral data depends on the target application given resolution trade-offs.
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- 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
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- 17. THEMATIC MAPS SVM on original HR data ( ground truth ) SVM on LR data 70.97% Finer Classification 74.32%
Editor's Notes
- 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.