Coarse-Fine classification of landsat image
using Neural Network
Neural network model
Due to simpleness of the calculation algorithm and excellent ability of learning , BP is widely and actively used for many fields [eg. [2]]. Fig. 2 shows a schematic diagram of neural networks for multi-input, multi-output. .
Fig. 2 Error Back Propagation
The number of the layers of BP is three. The values of the weight of the BP are initialized randomly. The data we use is Ishigaki Island (Japan) got from landsat 3 (4ch.). The images are classified by the window size. We call this method as window size method (WSM) for short. Windows for teaching are specified by a mouse on the Hyper frame plane with its category (sea, cloud, coast, plain).
.
input layer : (10x10pixel) x3ch.
hidden layer :10.
output layer : 5.
Display : sea-blue, cloud- red, coast-violet, plain-green.
Fig. 3 Original Image
Fig. 4 An example of classification with window
size 10 X 10
(3 Ch. are used. Number of categonies is 4.
Gray level of the color is propotional to the activation.)
Images of composite RGB image are shown in Fig. 3. Fig 4. shows the
results of classification. The gray level of the color is propotional
to the activation. Most parts of area are recognized correctly, but in
some part, fault. Table 1 is some of the detailed result of BP output layer activation levels (AL).
Table 1. Ssome detailed results of BP output layer
| |
Sea |
Cloud |
Coast |
Plain |
| 1 |
0.8937 |
0.0154 |
0.2000 |
0.0196 |
| 2 |
0.0058 |
0.0476 |
0.0682 |
0.9444 |
| 3 |
0.0603 |
0.1972 |
0.0780 |
0.1783 |
| 4 |
0.1006 |
0.1957 |
0.0745 |
0.1381 |
| 5 |
0.4451 |
0.0479 |
0.4551 |
0.0262 |
| 6 |
0.0117 |
0.4306 |
0.4554 |
0.0887 |
| 7 |
0.0045 |
0.2810 |
0.2440 |
0.4779 |
| 8 |
0.0385 |
0.4108 |
0.1694 |
0.0604 |
| |
Sea |
cloud |
coast |
pain |
| (ex.) No. 1 | 0.8037 |
0.0154 |
0.200 |
0.0196 |
This area (No. 1) is decided as "sea", because its active value=0.8937 is max.
AL of (No. 1) and No.2 are high and the difference between the maximum and second one (DBMS) is large. The classification is carried out correctly. AL of No. 3 and No. 4 are low and the DBMS is small. AL of No. 5 and No. 6 are relatively high, but the DBMS is small. AL of No. 7 and No. 8 are relatively high and the DBMS is relatively high. Many mis-classifications occur in cases of No.3-No.6. For these cases, we need to classify once more using smaller window.
