The detection of changes in an area over time using remotely sensed data such as images is referred to as change detection. It has a large range of applications. For example, changes in buildings can analysed for urban planning. Many conventional image processing and machine lear
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The detection of changes in an area over time using remotely sensed data such as images is referred to as change detection. It has a large range of applications. For example, changes in buildings can analysed for urban planning. Many conventional image processing and machine learning-based algorithms have been developed for the purpose of change detection. Conventional non-classification algorithms have advantages in their reduced computational cost. Remotely sensed images vary in their spatial resolution, which is the area a pixel covers on the Earth surface. This work aims to explore how the spatial resolution impacts conventional non-classification pixel-based techniques in the urban change detection context, to provide insight into their performance with regards to detecting urban-related change over different resolutions. A systematic experiment is conducted by considering the LEVIR-CD and OSCD test sets in their initial as well as multiple downsampled resolutions. The change detection algorithms Change Vector Analysis (CVA) and Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) are applied to the data individually. For creating binary change labels on a pixel-level, the Otsu algorithm is applied. A set of performance metrics is calculated, and trends in the metrics values over the resolutions are analysed. The data shows some trends towards improved metric values for lower spatial resolutions. The degree of the trends varies and is dependent on the algorithm and dataset. Overall, further research is necessary to consider influencing factors such as the amount of pixels in images, to refine the processing steps, and to broaden the scope of the experiment.