dc.description.abstract | Theoretically, change detections using remotely sensed images can be categorized into uni-temporal and multi-temporal image change detections according to the image properties and practical applications. In this study, the methods for the two categories of change detections using images of optical satellite sensors were proposed and applied to test their performance.
The uni-temporal image change detection concept is based on the extraction of spatial changes on the source image. Assuming that the area of change is limited when compared to the whole study area, the changed pixels on images can be regarded as an anomaly and detected; therefore, this study applied the theory of anomaly detection to detect specific spatial changes. To model and separate unchanged background and changed anomaly, the Expectation-Maximization (EM) theory and Bayes theory were applied. In addition, EM can be used to estimate the probability distribution functions (PDFs) of unchanged background and changed anomaly. Using the estimated PDFs, the theoretical accuracy of detected changes can be further estimated, and through the proposed spatial filtering process, the accuracy of detected changes can be further improved. In this study, the proposed method of uni-temporal image change detection was applied to detect oil slicks on the ocean surface. According to the analyzed results, the false alarms of detected oil slicks can be less than 10%. Furthermore, the false alarms of detected oil slicks can be improved to less than 5% if a spatial filtering process is further applied.
Multi-temporal image change detection is mainly used to detect the differences between images from different dates and extract the corresponding temporal changes. In general, change detection of multi-temporal images requires two essential steps: (1) normalization of images acquired on different dates and (2) detection of changed areas from normalized images. Images must be normalized because images from different dates can have significant radiometric differences caused by varying environmental factors during image acquisitions, specifically factors related to atmospheric effects. Theoretically, the radiometric calibration procedures can be used to adjust these differences, but the process is usually costly, labor-intensive, and unfeasible for frequent periods and large area operations. For change detection purposes, relative image normalization is a better choice because instead of calibrating the images of different dates to reflectance level, the images are relatively normalized to share the same reference level, which usually is sufficient to reveal the real temporal changes.
The most common approaches for image change detection usually compare images from different dates and derive some measurements to quantify the changes; thresholds are then set to extract the changed area. Nevertheless, the optimal thresholds can vary from case to case and remain a challenging issue. In this study, image normalizations were carried out by using pseudo invariant features (PIFs). Once the PIFs are extracted from source images, they can be used to perform the normalization process. For image change detection, we adopted a method from the concept of PIFs extraction to obtain a set of pseudo variant features (PVFs) corresponding to changed pixels. Once PVFs are found, they can be applied as a reference to detect the changes from the spectral differences derived by change vector analysis. The experimental results indicate that the proposed method can offer quality PVFs as a reference for detecting changes from images acquired on different dates. According to the experimental results with various image sets, the accuracies of change detection are around 90% with 0.8 kappa coefficients.
The major differences between proposed uni-temporal and multi-temporal image change detections are the procedures of deriving change measurement. In this study, in uni-temporal image change detection, the degree of anomaly was used as change measurement and obtained based on the statistics of normal image background. In multi-temporal image change detection, the change measurement is carried out by the spectral differences obtained from normalized multi-temporal images. However, the method for obtaining references for changed and unchanged objects should be the most important component of change detection algorithms. In this study, methods to find references for changed and unchanged objects were proposed for both uni-temporal and multi-temporal image change detections. In practice, these methods for obtaining references can not only be used in the change detection processes of this study, but can also be applied to other change detection algorithms that require references for changed or unchanged objects. | en_US |