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Poster Session
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The application of regional factors in the extraction of Remote Sensing information
The extraction of thematic information from TM image
The basic information of various surface targets contained in TM image is the spectral information, i.e. the brightness on each band of TM image. The main surface targets in this experimental area are paddy field, arid field, various type of water, settlement, reed field, reed-weed field, and mud-sand beach. The brightness of each surface target is shown in Fig. 1. On the basic of analyzing he brightness, this paper puts forward a multi-level threshold extraction method. The method takes into full account various types of water and easily flooded reeds, both the which have strong interest in the information system of flood danger forecast (called "system" below for short), the approach includes several steps:
Fig.1 Brightness diagram of surface target in TM image
- Extraction of water information
The basic requirement of the system is to extract various types of water efficiently and accurately. As shown in fig.1 the spectral difference between water and other surface targets is relatively obvious. By analyzing the histogram and comparing the extracted results. The researcher indicates that the ratio RM1/(TM3+TM7) combined with threshold method can extract water information more efficiently. The result is shown Fig. 2. The red are expresses water field extracted by threshold method. The white area shows the difference between the water field extracted respectively by the cluster classification method and by threshold, method. As compared with the boundary given by the cluster classification, the boundary drawn by threshold method coincides more accurately with the real condition. In order that different types of water can be further distinguished, a fig. 2 Water area extracted from, ratio image TM5/TM4 is from TM image produced for it has a high sensitivity to mud sand content in water. On the principle of multi level classification, water can be divided into several types by means of threshold method, including: the river water containing low mud-sand content, and the lake water containing low mud-sand content, and the lake water of which mud sand content in quantity is between the two types of water mentioned above. The classification tree is shown in Fig 3 Fig. 4 gives the classification results. The results indicate that all types of water have been accurately classified, especially the river water, the main parts of flood, having much more satisfying accuracy of classification.
Fig.2 Water area extracted from TM image
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