Neural Approah to Remote Sensing Image classifiation
System configuration
We used a personal computer based system; computer (NEC PC9801 RA), neural (ImPP) board (NEC), Neuro07 software (NEC Information Technology Co. Ltd.), Hyper Frame (Digital Arts Co. Ltd.) and co-processor (80387)
(Fig.2).
The classification of image is carried out at high speed by ImPP and co-processor.
Fig.2 System configuration by personal computer
Extraction of cluster feature
Though what we use the image features for input of the BP has not been well studied up to now, we use gray level raw data and their differentials (by Sobel-operator) within the window area.
Neural network model
The number of the layers of BP is three. The initial values of the weight of the BP is initialized randomly. The data we use is Ishigaki Island (Japan) got form Landsat 3 (4ch.). Windows for teaching are specified by a mouse on the Hyper frame plane with its category (sea, cloud, coast, plain).
Input layer: (10X10) x 3ch
hidden layer: 10
output layer : 4
display: sea-blue, cloud-red, coast-violet, plain-green
Images of Ch0, Ch1, Ch2 and composite RGB image are shown in
Fig. 3. (a)- (d).
Fig. 4 is the profile of leaning process by ImPP board;
(ex.) No. 8 heiy Þ kaig (0.245), umi (0.190)
No. 15 kaig Þ kaig (0.455), umi (0.146)
here, umi, kaig, heiy and kumo means sea, coast, plain and clud respectively. No. 8 is the case that BP understand kaig (0.245) and umi (0.190) at this point for the true category " heiy", and displayed as red character for the mark of failure. No. 15 is the succeeded case.
Fig.3 Ishigaki Island [ Landsat 3 (Ch.4) ]
Fig. 5 shows the result of classification. Most parts of area are recoognized correctly, but in some part, fault.
Table.1 is some of the detailed result of BP output layer.
| |
Sea |
cloud |
coast |
pain |
| (ex.) No.1 |
0.8937 |
0.0154 |
0.200 |
0.0196 |
This area (No.1) is decided as "sea", because its active value=0.8937 is max. In cases of No. 5 and No. 6 the difference between max. and the second one is small. For these cases, we need to classify once more using smaller window.
Fig.4 Profile of learning process by ImPP board
Fig.5 Result of classification by BP
Conclusive remarks
In this paper, we discussed about the classification o multi-spectral remote sensing images by BP using gray and differential values of the image. The more the size of widow area, the more we easily recognize the features of the area, but spend much more time for learning instead.
We programmed the BP on PC-9801 RA and Excel image processing unit (Avionix Co.Ltd.) using Lattice-C. Next, as the second stage we made up the present system using Hyper frame memory card instead of the Excel using MS-C for cost down. We need not necessarily use the ImPP board, but can process the image more than 10 times speedy using it.
Reference
- Tamura S. et.al: Nauro-Voice-Recognition Jointly Using Mouth Shape Image and Voice Features , IEICE in Japanese, PRU89-19, pp. 1-8,1989.
Table.1 Some of the detailed result of BP output layer
|
Sea |
Cloud |
Coast |
Plain |
| 1 |
0.8937 |
0.0154 |
0.2000 |
0.0196 |
| 2 |
0.0058 |
0.0682 |
0.0682 |
0.9444 |
| 3 |
0.0603 |
0.1972 |
0.0780 |
0.1783 |
| 4 |
0.1006 |
0.1957 |
0.0745 |
0.01381 |
| 5 |
0.4451 |
0.0479 |
0.4551 |
0.0262 |
| 6 |
0.0117 |
0.4306 |
0.4564 |
0.0887 |
| 7 |
0.0045 |
0.2810 |
0.2420 |
0.4779 |
| 8 |
0.0385 |
0.4108 |
0.1694 |
0.0604 |
