This PhD research is focused on urban change detection using very high resolution (VHR) imagery acquired by different sensors (i.e. airborne and satellite sensors) and different view angles. Thanks to high amount of details provided in VHR images, urban change detection is made possible. On the other hand, due to the complicated structure of 3D urban environments when projected into the 2D image spaces, detection of changes becomes complicated.

In general, change detection is divided into two major steps:​

1. Establishment of a relation between bi-temporal images so that the corresponding pixels/segment are related;​ this is called co-registration;​
2. Comparison of the spectral properties of the co-registered pixels/segment in the bi-temporal images in order to detect changes.

As far as Step 1 is concerned, establishment of an accurate global co-registration between bi-temporal images acquired by the different sensors is not possible in urban environments due to different geometric distortions in the imagery. Therefore, the majority of studies in this field avoid using multi-sensor and multi-view angle images.

In this study, a novel co-registration method called "patch-wise co-registration" is proposed to address this problem. This method integrates the sensor model parameters into the co-registration process to relate the corresponding pixels and, by extension, the segments (patches).

In Step 2, the brightness values of the matching pixels/segments are compared in order to detect changes. Thus, variations in the brightness values of the pixels/segments identify the changes. However, there are other factors that cause variations in the brightness values of the patches. One of them is the difference of the solar illumination angles in the bi-temporal images. In urban environment, the shape of the objects such as houses with steeply-sloped roofs (steep roofs) cause difference in the solar illumination angle resulting in difference in the brightness values of the associated pixels. This effect is corrected using irradiance topographic correction methods. Finally, the corrected irradiance of the co-registered patches is compared to detect changes using Multivariate Alteration Detection (MAD) transform.

Generally, in the last stage of change detection process, "from-to" information is produced by checking the classification labels of the pixels/segments (patches). In this study, a fuzzy rule-based image classification methodology is proposed to improve the classification results, compared to the crisp thresholds, and accordingly increase the change detection accuracy.

In total, the key results achieved in this research are:​

1. Including the off-nadir images and airborne images as the bi-temporal combinations in change detection;​
2. Solving the issue of geometric distortions in image co-registration step, caused by various looking angles of images, by introducing the patch-wise co-registration;​
3. Combining a robust spectral comparison method, which is the MAD transform, with the patch-wise change detection;​
4. Removing the effect of illumination angle difference on the urban objects to improve change detection results;​
5. Improving classification results by using fuzzy thresholds in the image classification step.

The outputs of this research provide an opportunity to utilize the huge amount of archived VHR imagery for automatic and semi-automatic change detection.

Automatic classification of the images especially in urban area is still a challenge due to the spectral similarity between urban classes such as roads and buildings. Therefore, generation of the accurate “from-to” information is still remaining for future researches.