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Poster Session Q
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Urban thematic information extraction and dynamic extension detection
The contextual method just uses that context to improve accuracy in classifying remotely sensed data. It can be applied not only to original multispectral digital data but also to the classified data to reclassify its landuse. This paper uses reclassification considering large number of contextual pixels. Fig.2 demonstrates the reclassification procedures.
- Dynamic Clustering
Dynamic clustered image can give various spectral clusters which reflect land covers and their environments. Becuse clustering has some correlation with the selection of orginal clustering centers, we train some typicalm samples to compute the original centers in order to reach the best clustered results. Some essential division and mergence between classes are also made in clustering (details please refer to {1}.
Considering the terrain a land cover of Yueyang urban area, wse select 13 classes as training samples. In terms of their spectral features, TM1,3,4,5 bands are used for clustering . Photo 1 is the clustered image of TM data after several split and mergence between classes.
In photo 1, blue part(code 1) is water-body, green part (code 2) is rural land, the grey (code 7) is the old urban, the yellow (code6) is the new urban and the bright yellow (code 14) corresponds to the new building storehouse and open land (BSO) . The distribution of the pink part and its spectral features (See fig.3) shows that most of the urban and non-urban land use (Such as G,C) except some noises (Such as A,B,E,F,etc.) are the mistral class.
- Contextual information Extraction and urban Landuse Reclassification
Analysis indicates that there are four problems in the clustered image (1) There exists a mixtural class between the urban and nonurban landuse. (2) Separate water points is presented in the urban area. (3) there are some urban points in the non-urban area and some non-urban points in the urban area. (4) The targets in the urban area is complicated that some old urban poits exist in the new urban area while several new urban points are in the old urban area etc.
For the reason above, a new contextual method is designed to improve the accuracy of the classification in which multi-frequency vectors, one form of the contextual information (See Fig.4) are used.
Supposing P1,P2,P3 and P4 are the frequencies of old urban, new urban, BSO and non-urban land respectively, we give the following discrimination criterion to x,a point of the mixtural class. that is:
Fig.4 With four possible classes, the frequncies corresponds to the gibven window is(5 3 0 1) T
P4-(P1+P2+P3) > B X e non-urban
P4-(P1+P2+P3) <-B X e i, for Pi = max (P1,P2,P3,P4)
(P4-(P1+P2+P3) < -B don't be discriminated
The threshold B is determined according to the histogram of the value P4-(P1+P2+P3) of the training samples.
It is known that contextual reclassification is affected by window size as well as component frequencies change with the window size.
Fig.5 Correlation between reclassification accuracy and window size
Wharton tested this problem seriously. He used two subsences, 100x100 and 500x500 pixels. The acquired correlation curve line between reclassification accuracy and window size are shown in Fig. 5 . When window size increases, the reclassification accuracy also increases, then reachs the top at one point. as the window size increases further more, the accuracy decreases inversely. This is because the large window introduces some non-contextual even wrong contextual pixels.
It is difficult to determine the best window size in the practical classification. This paper, therefore, presents a multi-frequency vector algorithm to solve the problem. several windows which can express context most approximately are selected. These windows are applied to the pixels of mixtural class, in which the pixels that can not be discriminated by small window size are reclassified by larger windows frequency vector. Three windows, 7x7, 11x11 and 15x15 as in Fig. 6 in which weight coefficients decrease while the distance increases, are selected according to Fig.5
Fig.6 Windows with changed coeffecients
Fig.7 Flowchart of reclassification using multi-frequency vectors
Fig. 7 is the flow chart of the reclassification of the mixtural class. The procedures are also used to other three problems (2), (30 and (4) as stated above Photo 2 is the improved urban the metic map.
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