Associating Anomaly Detection Strategy Based on Kittler’s Taxonomy with Image Editing to Extend the Mapping of Polluted Water Bodies

Abstract

Anomaly detection based on Kittler’s Taxonomy (ADS-KT) has emerged as a powerful strategy for identifying and categorizing patterns that exhibit unexpected behaviors, being useful for monitoring environmental disasters and mapping their consequences in satellite images. However, the presence of clouds in images limits the analysis process. This article investigates the impact of associating ADS-KT with image editing, mainly to help machines learn how to extend the mapping of polluted water bodies to areas occluded by clouds. Our methodology starts by applying ADS-KT to two images from the same geographic region, where one image has meaningfully more overlay contamination by cloud cover than the other. Ultimately, the methodology applies an image editing technique to reconstruct areas occluded by clouds in one image based on non-occluded areas from the other image. The results of 99.62% accuracy, 74.53% precision, 94.05% recall, and 83.16% F-measure indicate that this study stands out among the best of the state-of-the-art approaches. Therefore, we conclude that the association of ADS-KT with image editing showed promising results in extending the mapping of polluted water bodies by a machine to occluded areas. Future work should compare our methodology to ADS-KT associated with other cloud removal methods.

Keywords

• anomaly detection
• image editing
• pollution mapping
• machine learning
• Kittler’s Taxonomy
• water bodies