Coarse-Fine classification of landsat image
using Neural Network
Coarse-Fine classification
The resolution corresponding to one pixel of landsat image data used in the experiments is 80 m. For the purpose of increasing the classification accuracy, the experiment of reducing the window size of learning/ recognition to a pixel was done. However, in this case the learning has not converged. In BP processing by pixel unit, the method using pseudo-variance in neighboring 3x3 window can be considered, and it's now under consideration. Alternatively, it can be considered that the weight of recognizing window of 10x10, 5x5, 3x3, 2x2 size etc. which have clearly obtained by the learning, in used to classify the center pixel of the window by moving one by one. We call this method as window center method (WCM) for short. .
Although there is a problem of processing time (about 3.5 hours), it is done and compared with the original image. Then it agreeded well with inspecting consideration. Using the BP classifying the data based on the pattern shape, the experiment for the size of 5x5 (about one hour) become better in the boundary and compound region (WCM). The obtained results are good one the whole. The classification (3 ch. and 4categories) results are shown in Fig. 5.
Fig. 5 Result of WCM ( Window 5 X 5, 3 Ch. Number of categories is 4. )
Therefore, the learning window size is not simply reduced, but it is taken largely in ample uniform regions. The window size of 5x5 is applied in complicated boundary
or compound regions, hereupon, and the category of the center point of window is determined one by one by moving the window. In this way, the coarse0fine classification has been done by the multiple window size. Fig. 6 and Fig. 7 show the results of classification using 4 channel image data. Shadow of cloud category is added and displayed by black. Window size is 5x5 and 3x3 respectively. Classification is done by WSM and WCM. In Fig. 8, it is shown the gray leveled results: first; it is classified initially by 10x10s9ze (WSM). second; the WCM is applied to re-classify by 5x5 sized for the regions with lower activation than 0.6 As the classification results the activation in the boundary and compound regions gets higher value than that of obtained from the experiment with one size, and the classification accuracy becomes higher and reduction of the processing time ( about 35 minutes) is possible. The more the size of window area, the more we easily recognize the features of the area, and spend less time for learning, but the classification becomes coarse instead. .
Fig. 6 Classification by 4 Ch. 5 categories
Fig. 7 Classification by 4 Ch. 5 categories (the same as Fig. 6)
