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Forestry / Vegetation
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Classification of Multi-sensor Data using a combination of Image Analysis Techniques
Neural Network with SPOT data (3 bands) + coherence map
The result of this classification is shown in Table 2.
Table 2. Neural Network SPOT B,B2,B3 + Coherence Map
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| BS | SB | S | F | M | G | W | R | uncl | | | ACC |
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| BS | | | 189 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | | 1.00 |
| SB | | | 0 | 351 | 0 | 0 | 0 | 17 | 0 | 0 | 3 | | | 0.95 |
| S | | | 0 | 0 | 251 | 0 | 0 | 0 | 0 | 0 | 0 | | | 1.00 |
| F | | | 0 | 0 | 0 | 217 | 0 | 0 | 4 | 29 | 3 | | | 0.86 |
| M | | | 0 | 0 | 0 | 0 | 45 | 18 | 0 | 112 | 0 | | | 0.29 |
| G | | | 0 | 0 | 0 | 0 | 61 | 382 | 0 | 45 | 0 | | | 0.78 |
| W | | | 0 | 0 | 0 | 0 | 0 | 0 | 414 | 0 | 0 | | | 1.00 |
| R | | | 0 | 0 | 0 | 0 | 24 | 10 | 0 | 53 | 0 | | | 0.61 |
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| REL | | | 1.00 | 1.00 | 1.00 | 1.00 | 0.35 | 0.89 | 0.99 | 0.22 | | | |
average accuracy = 80.66%
average reliability = 80.66%
overall accuracy = 85.37%
From table 2 it can be concluded that the Coherence map has a positive influence on the classification.To improve the influence of this map, the following classification was executed:
Neural Network with SPOT data (2 bands) + coherence map
The results were:
average accuracy=84.22%
average reliability=82.58%
overall accuracy=87.12%
Because the neural the neural confuses some classes that had been almost perfectly classified by the maximum likelihood, the influence of a classification in two steps was investigated. The four classes that were indicated with high accuracy by means of the maximum likelihood classifier were masked out and the remaining classes were masked out and the remaining classes were classified combined classification gave the result shown in Table 3.
Table 3. Maximum Likelihood + Neural Network + SPOT B1,B2 +Coherence Map
| | BS | SB | S | F | M | G | W | R | uncl | | | ACC |
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| BS | | | 189 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | | 1.00 |
| SB | | | 0 | 356 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | | | 0.98 |
| S | | | 0 | 0 | 251 | 0 | 0 | 0 | 0 | 0 | 0 | | | 1.00 |
| F | | | 0 | 0 | 0 | 231 | 11 | 0 | 0 | 8 | 3 | | | 0.91 |
| M | | | 0 | 0 | 0 | 0 | 120 | 10 | 0 | 45 | 0 | | | 0.69 |
| G | | | 0 | 0 | 0 | 0 | 86 | 360 | 0 | 33 | 0 | | | 0.74 |
| W | | | 0 | 0 | 0 | 0 | 0 | 0 | 414 | 0 | 0 | | | 1.00 |
| R | | | 0 | 0 | 0 | 0 | 26 | 2 | 0 | 59 | 0 | | | 0.68 |
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| REL | | | 1.00 | 1.00 | 1.00 | 1.00 | 0.49 | 0.96 | 1.00 | 0.41 | | | |
average accuracy = 87.48%
average reliability = 85.31%
overall accuracy = 89.27%
Conclusion
Overall accuracy is the highest for the combined classification of optical and microwave data. This holds particularly for those classes that had not been very accurately classified by the maximum likelihood classification of the optical data alone. Improvements were found for the classes maize, grass and reed, which had been poorly classified in the first experiment. Integration of the radar data was the successful in the coherence map format. The reason for this is the large variation homogeneous areas due to speckle, which influence is reduced in the coherence map. Finally, masking several classes that had been classified already with high accuracy, improves overall accuracy because of 1) the logical increase of the reliability in the confusion matrix and 2) the reduction in possible classes in the second pass. Investigations still need to be carried out in more detail as to whether this improvent is consistent for other areas.
References
- Benediktsson , J.A., Swain, P.H., and Ersoy, O.K. 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data, IEEE Transaction on Geosciences and Remote Sensing, 28, pp 540-551.
- Civo, D.L., 1991. Landsat TM image classification with an artificial neural network. Proceedings ASPRS-ACSM Annual Meeting, Balimore, MD. Vol. 3 pp. 67-77.
- Huurneman, G.C. Gens, R., and Broekema, L., 1996. Vienna, Vol II- p170.
- Schwablisch, M. and Winter, R., 1995, Erzeugung digitaler Gelandermodelle mit Methoden der SAR-Interferometry. DLR Nachrichten, Heft 79 (August 1995), pp 20-24.
- Wegmuller, U. and Werner, C.L., 1994Analysis of interferometer land surface signatures. Proceedings PIERS'94, Noordwijk, The Netherlands, Paper Code 039.
- Zebker, H.A. Werner, C.L., Rosen P.A. and Hensley, S., 1994. Accuracy of Topographic Maps Derived from ERS-1 Interferometric Radar. IEEE Transactions on Geoscience and Remote Sensing, Vol. 32 No 4, July 1994.
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