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Poster Session
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The estimation of cotton-growing areas by Remote Sensing
Classification
We used the maximum likelihood method to conduct the classification. The steps are as follows:
- Preparation of Classification
Before classifying, the digitizer was used to load the administrative boundary of Huojia country mapped in the topographic maps at a scale of 1:50,000 into computer so as to form a 2-value image called boundary file. The image presented a rectangle with 512X1024 pixels. Each pixel was assigned the value 0 or 1, e.g. the pixels; value of outside country-boundary was equal to 0, and inside equal to 1.
At first, by using 3 X 3 pixels sampling method, the spectral information of bands 2,3,4, was extracted and the edge enhancement was conducted. Then, based on hydrographic net and network of communication lines, five well-identifiable and correspondent points on the image and topographic maps were selected as control points to dealt with location, enlargement and whirling, etc. it makes the homologous points of the image and topographic maps overlap each other; Finally, we selected images of bands 2,3,4 that were unanimously as the boundary file form bands 2,3,4 and treated them as the objects of classification. The objects multiplied by the boundary file was the result that all the values of pixels outside the Huojia country were 0, it was analogous to a special type.
- Determination of the number of types
In the classification, cotton was regarded as a major type, named RED2, its corresponding cropsi.e. soybean, wheat, and sweat potato were named RED 1. RED3, and RED4 respectively; water area and residential area named WATER and VI; the other objects were merged into one type, named OTHER. So we get seven types.
- Encircling of Training Sites
In the TM false colour composite images of bands 2,3,4 it was very easy to distinguish the water area and residential area, we used a tracking boll to encircle these two types of training sites. In the TM false colour composite images, the crops of cotton, wheat sweat potato and soybean, etc. display different thick or thin red tones. Based on the result of colour enhancement and cluster analysis of bands 2,3,4 colour composite image and complied with the ground truth data and the result of spectral analysis, we encircled the training sites one by one.
After having compared and revised repeatedly and determined six types of training sites, we conducted clustering process once again and counted the emergence probability of this six types in order to provide parameters for normal classification.
- Classification based on the Maximum Likelihood Method
The result of supervised classification for the first time was not all of them unanimous as the ground truth. After having revised the limits of classification, we conducted the maximum likelihood method nice again, and got the numbers of pixels of seven types (Table 2) and the special distribution of each type (Fig. 3) finally.
Table 2. The result of TM Classification by
maximum likelihood method
| Type order | Type name | Pixel | Percentage |
1
2
3
4
5
6
7 |
Water
Red1
Red2
Red3
Red4
VI
Other |
88
4856
6696
23034
8489
18319
0 |
0.14
7.90
10.90
37.80
13.80
29.80
0.00 |
| Total | 61482 | 100.00 |
Fig. 3 The map of classification
- Analysis of Accuracy
According to the figures of ground survey in Huojia country, the cotton fields were 11% of the total area of the country in 1986. In this classification, we got 6696 pixels of cotton fields, 10.90% of the total area of the country. If the data of ground survey were true values, the classification accuracy was 97.43%.
Houjia country lies on the Northern Henan Plain and suitable for cotton planting. In 1986, cotton was mainly planted on its areas of mound land of Xunfengling in Midwest, low-lying land linked with Taihang mountain in the north, the side dyke depression of ancient Yellow river in the central part and the lands around the residential areas of the side dyke depression of ancient Yellow river in the east. The result of the classification was identical with this distribution situation.
References
- Institute of cotton of Chinese Academy of Agricultural Science, Cultivation Science of Cotton in China, page 54. Shanghai publishing house of science and technology, Dec. 1983.
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