The Key Technology Of Change Detection Using High Resolution Remotely Sensed Imagery

High resolution imagery can provide more execute detail information, and have been widely used in scientific application. However, due to heterogeneity, texture, shadow and mixed pixel problem, there are big challenges in change detection using high resolution remotely sensed imageries. Additionally, spatial mis-registration, shadow and noise are common drawbacks of pixel-based method and particularly conspicuous in high resolution imagery, which can exaggerate or make thematic changes in change detection analysis. Considerable effort had been expended on minimizing the influence of these problems. In this study, we focused on these big issues and did the research on the key technology of change detection using high resolution remotely sensed imagery.(1) With respect to the inevitable mis-registration and shadow effects on change detection analysis, we proposed an object-based post-classification of the Multivariate Alteration Detection components method (OB-MAD), which can take advantage of pixel and object-based method. The extended difference image generated from MAD components can enhance the change information as well as amplyfying the false change caused by geometric distortion and shadow to an extent. On basis of the good shape information of false change, we proposed to do the object-based post-processing on this extended difference image. Obviously, aerial photograph with a high resolution less than 1 meter were useful in change detection, which can provide the real time multi-temporal imageries. However, compared with widely usd satellite imageries, the geometric distortion, shadow and noise problems of aerial imagery were significant. Change detection on wetland is a big topic in environmental monitoring and it is very difficult to detect the changes of vegetation and exclude the false information. Thus, very high spatial resolution images of drained managed wetland ponds (Gadwell North) were used to compare the proposed OB-MAD method with three commonly used classification methods in terms of minimizing the influence of mis-registration and shadow on the change detection analysis:(1) the traditional MAD method with thresholds (Threshold-MAD), (2) a pixel-based post-classification of MAD components with decision tree analysis (PB-MAD), and a traditional object-based post-classification method (OB-traditional). The proposed OB-MAD method, which utilized shape and textural information of objects derived from MAD components, produced the highest accuracy with respect to wetland change detection and successfully minimized the influence from the geometric distortion and shadow on the changed area. Overall accuracy was best for the OB-MAD method (93.54%), followed by the Threshold-MAD (90.07%), and PB-MAD (86.09%). The OB-MAD method also resulted in the greatest user's accuracy for no-change pixels (90.57%), compared to 82.2% and 81.49% for PB-MAD and Threshold-MAD, respectively.(2) Inevitable geometric distortion of multi-temporal imageries and mixed pixel can significantly influence the accuracy of pre-processing in change detection analysis. New pixel represented by the averaged features within the window size can provide more reliable information, which had been widely used in texture extraction and filtering, since we proposed to combine the window size into the automatic relative radiometric correction methods. In our study, we tried to find out whether the proposed window size based relative radiometric correction method can improve accuracy. In addition, due to the fact that the accuracy of relative radiometric correction is different at homogeneous and heterogeneous landscape, we tried to analyze the sensitivity of window size at different landscapes. Homogeneous and heterogeneous landscapes generated from SPOT-5 imageries of 2005 and 2006 of PanYu city, China was preferred as our study areas. Three window size with 3 x 3,5×5 and 7 x 7 pixel, and two relative radiometric correction methods named MAD and robust regression were used to compare with the traditional manual method using two spatial resolution imageries of aerial image and SPOT satellite iamge. All of the results indicated that the optimun window size of proposed relative radiometric correction was significantly different at homogeneous and heterogenous landscapes. Specifically, for the homogeneous landscape, the optimun window size was 3 x 3 pixel, while 5×5 pixel window size was selected at heterogeneous landscape. Moreover, compared with traditional manual method at local size, robust regression was significantly different at both landscapes, while MAD method got robust better results. The proposed window size based relative radiometric correction method was sensitive to the spatial resolution of images.(3) Traditional pixel-based change detection method using difference image is difficult to perform change on multi-sensor imageries, because of the different number of bands and the impact of inter-channel correlation. In addition, pixel-based change detection methods are more sensitive to the inevitable geometric distortion, shadow and noise in pre-processing for high resolution imageries, and often suffer from "salt and pepper" effect in the resulting map. Note that another bottleneck is how to reasonably determine the threshold of change and no-change of difference image. In this context, we proposed an object-based method based on difference image generated from objects (OB-EM). Firstly, we got the object-based difference image using MAD and MNF method, and then found the change and no-change information using EM method. The proposed OB-EM method can take advantage of salient aspects of the MAD, MNF and object-based methods. SPOT-5 imagery of 2005 and 2006 in a case study of PanYu city, China were used to validate the proposed OB-EM method by comparing with other methods (DFPS, Gams, OB-traditional and OB-MAD). All the results indicated that feature selection can significantly improve the accuracy of change detection. Second, the proposed OB-EM method can take advantage of MAD, MNF and EM, which could deal with the data getting from different sensors. The "salt-and-pepper" effect could be improved well. Moreover, the accuracy of pre-processing of change detection was not required as high as OB-MAD. Finally, Z-test got a significant result between OB-EM and OB-MAD. It demonstrated that OB-EM was such a better method that can obviously recognize the false change from mis-registration and shadow.