Detection method for building change by multispectral image

The use of object-oriented classification is mainly manual participation, and the process is cumbersome.

[0010] Wang Min et al. proposed an image change detection method based on multi-feature evidence fusion. By calculating the structural similarity of the texture,...

As can be seen from Table 2, relative to using pixel-level change detection alone and using texture feature change detection alone, the multispectral image building change detection method proposed by the present invention has a correct rate that has increased by about 15% and 7% respectively, The false detection rate has been significantly reduced, by about 41% and 31% respectively. Since the feature-level change detection is aimed at the change of the object of interest, the false detection rate is much lower than that of the pixel level. Combining the pixel level and the feature level can Reducing the impact of non-changing areas can achieve better detection results. Because the building shadow is only a small part on the image, the method for removing the shadow still has certain limitations; the method of the present invention has a certain improvement in accuracy compared with the multi-feature D-S fusion method, and the false detection rate also decreases to some extent; and object-oriented Compared with the comparison after classification, the correct rate of change detection is increased by 5% after the combination of pixel level and feature level, and the false detection rate is reduced by about 10%. To a certain extent, the change of ground object pairs similar to the building spectrum is reduced detection impact.

The experimental result of comparative analysis above six kinds of methods, can see, use pixel ratio method to carry out the change area range that change detection obtains the largest, it not only has included the change of building, and has included other features around the building From the two time-phase original images, it can be seen that the ground objects such as roads and vegetation around the building have changed greatly, and the change detection using the image pixel information alone cannot obtain the building change information. The effect of extracting texture features for building change detection is significantly better than the pixel ratio method. First, extract the texture features of buildings in two phases, and then compare the extracted building areas pixel by pixel for change detection. This method makes full use of The features of the building on the image are used to detect the change of the building, but there are some areas on the image with similar texture features and buildings. After threshold segmentation, these areas are retained as building areas and participate in the pixel-by-pixel comparison process. , these regions form part of the change detection spurious detection. Detecting changes in building shadows and removing the influence of shadows in images of texture feature changes can reduce the false detection rate to a certain extent. The influence of object shadows should be very small; in areas where buildings are densely arranged, building shadows are blocked by buildings, and the influence of shadows is limited. Effectively improve the detection effect. The comparison method after object-oriented classification first detects the change area by using the pixel ratio method on the image, and then uses the spectral characteristics of the image to classify the ground objects in the change area. Merge into one category, and then perform change detection. Due to the phenomenon of the same object with different spectra and different objects with the same spectrum in remote sensing images, it is easy to cause classification confusio...