×

You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Title
Mapping without the sun
Author
Zhang, Jixian
On the other hand it could be assumed that a SVM performs better, because it achieves the highest results when classifying single-sensor data. This assumption is confirmed by the experimental results: The separate training of the SVM and the fusion by another SVM further increases the classification accuracy up to 76.5%. Classifier algorithm SAR TM SAR+TM MLC 55.5 66.0 70.1 DT 46.0 63.5 64.0 DT boost 61.5 67.7 75.6 SVM 62.7 68.2 74.2 SVM Fusion - - 76.5 Table 2. Overall accuracies [%], using different classifier algorithms and data sets The achieved overall accuracies clearly illustrate the value of using multisensor imagery for classification. Comparing the multisensor classification by SVM, the accuracy is increased by up to 6% compared to the results of the Landsat image and up to 11.5% compared to the classification accuracy achieved with the SAR imagery. The positive impact of multisensor imagery on the classifier performance is also confirmed by the class- specific accuracies (Table 3 and Table 4). Land cover Class Input data SAR TM Arable crops 51.2 56.2 Cereals 73.4 75.0 Forest 74.6 89.4 Grassland 71.0 56.8 Orchards 54.8 47.6 Rapseed 58.2 73.4 Root crops 58.2 66.0 Urban 60.2 81.4 Table 3. Class-specific accuracies, using single-source imagery, bold numbers indicates best results Land cover Class Classifier algorithm DT boost. SVM Fusion Arable crops 70.6 70.8 Cereals 77.8 76.6 Forest 93.2 91.2 Grassland 74.2 72 Orchards 63.6 68.6 Rapseed 76.0 75.2 Root crops 66.4 73.8 Urban 83.0 84 Table 4. Class-specific accuracies, using a boosted DT and SVM Fusion, bold numbers indicates best results On one hand the single-source results show that the two data sources are differently reliable. Whereas grassland and orchards are more accurately classified using the SAR data, the other classes are better described by the Landsat image. Moreover all class-specific accuracies are improved by the use of multisensor imagery. The accuracies of classes that achieve relatively low values on both image sources (e.g., arable crops, orchards) are significantly increased by the multisensor approach. The comparison of the class-specific accuracies by a boosted DT and the proposed fusion strategy demonstrate that the results from the SVM Fusion are more balanced (Table 4). Whereas the accuracies vary between 63.6% and 93.2% for the boosted DT, the accuracies achieved by the SVM Fusion vary between 68.6% and 91.2%. The visual assessment of the classification results underlines the positive effect of multisensor fusion and the complementary character of the multitemporal SAR and the multispectral optical data (Figure 2): The resulting maps seem more homogeneous in some regions and errors for critical classes are decreased. Contrary to the visibly good performance of the multisensor fusion, the disappearance of the highway in the middle of the map subset, which was recognized in the TM data, shows possible drawbacks of the multisensor approach. Figure 2. Classification results, using different data sets 6. CONCLUSION The challenge of classifying multisensor data sets, containing multitemporal SAR and multispectral imagery has been addressed. In experiments, the separate training and further fusion by a SVM outperformed all other parametric and nonparametric classification techniques including a conventional SVM. We assume the concept to be different imagei equally reliable more adequate source individi function on the by the first S transformation, space that cons rule images. T! original imager to describe the i Moreover it h< classifiers in tei data sets. Henc proposed classi classifier perfc segmentation a would be wort! increased multii Generally, the r multitemporal worthwhile. He increased by si applied classify approaches for ongoing researc reference: Benediktsson, J of Multisource Fusion. IEEE I pp. 1367-1377. Benediktsson, J Network Apj Classification i Trans. Geoscie) Blaes, X., Vaa crop identificat Remote Sensing Briem, G.J., E Multiple Class Data. IEEE Trc 2291-2299 Brown de Cc Commisso, K., vegetation map decision tree cl pp. 316-327. Camps-Vails, Frances, J., Q hyperspectral Remote Sensing Chen, C.-C. £ support vec http://www.csii