Classification of Multi-sensor Data using a combination of Image Analysis Techniques
3. Image Analysis
The datasets were classified with the use of the maximum likelihood classifier of the ILWIS software and the neural network classifier of the PCI software.
Table 1. Maximum Likelihood, SPOT B1, B2, B3
| | BS | SB | S | F | M | G | W | R | uncl | | | ACC |
|
| |
| 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 | 221 | 0 | 0 | 0 | 29 | 3 | | | 0.87 |
| M | | | 0 | 0 | 0 | 0 | 46 | 1 | 0 | 128 | 0 | | | 0.26 |
| G | | | 0 | 1 | 0 | 0 | 114 | 326 | 0 | 47 | 0 | | | 0.67 |
| W | | | 0 | 0 | 0 | 0 | 0 | 0 | 414 | 0 | 0 | | | 1.00 |
| R | | | 0 | 0 | 0 | 0 | 57 | 1 | 0 | 29 | 0 | | | 0.33 |
|
| |
| REL | | | 1.00 | 1.00 | 1.00 | 1.00 | 0.21 | 0.98 | 1.00 | 0.12 | | | |
average accuracy = 76.52 %
average reliability = 78.98%
overall accuracy = 82.63%
In general, neural network classifications do not automatically recognise a NULL class. This means that all pixels are appointed to one of the classes for which the network was trained; also pixels that belong to a non-sampled land use will be classified but the wring class. A mask was created using the NULL class of the maximum likelihood classification to avoid this.
Maximum Likelihood with SPOT data.
For the quality assessment of the classification methods applies for databasets in different methods applied for datasets in different compositions, the classification of the three SPOT XS bands by means of a maximum likelihood classifier was used as a reference. The result of this classification is shown in the confusion matrix in Table. Eerro.
Neural Network with SPOT data
To compare the strength of the neural network classifier in alalysing the same input, a neural net of 3 layers with 3, 8, and input, a neural net of 3 layers with 3,8, and 8 nodes, respectively, was trained in a varying number of iterations (500-2500). This experiment showed that after 500 iterations the network had already been trained. The overall accuracy of the classification using this network was almost identical to that of the maximum likelihood classifier.
average accuracy = 74.19%
average reliability = 77.06%
overall accuracy = 82.68%
Maximum Likelihood + Neural Network with SPOT data.
The second experiment was executed to investigate the performance of a hybrid classification.
The classes with the highest accuracy were considered to have been sufficiently identified. The pixels belonging to these classes, including the NULL class, were used to create a mask. Next the remaining pixels were consisted of 3 layers with 3 inputs, 4 nodes in the hidden layer and 4 outputs.
The results of this experiment were :
average accuracy = 75.48%
average reliability = 76.07%
overall accuracy = 82.68%
After these experiment we concluded that, with these datasets, the use of a neural network will not improve the results of the classification of this area.
In the following experiments, radar data was included in the classification. We investigated in which mode these data contribute best to the applied classification methods.
Neural Network with SPOT data (3 bands) + ERS1/2 intensity images
The feature space was here increase to a 5-dimensional one. The results of this combination were :
average accuracy = 78.39%
average reliability = 77.95%
overall accuracy = 84.69%
The results of the application of the maximum likelihood to this data combination were even worse, and were caused by the high variance of the intensity images due to speckle. This high variation gives a large spread in the feature space which results in outliers. These outliers are identified as NULL class elements by the maximum likelihood classifier, resulting in a lower accuracy of the classification.
