Dimensionality Reduction and Anomaly Detection Based on Kittler’s Taxonomy: Analyzing Water Bodies in Two Dimensional Spaces

Abstract

Dimensionality reduction is one of the most used transformations of data and plays a critical role in maintaining meaningful properties while transforming data from high- to low-dimensional spaces. Previous studies, e.g., on image analysis, comparing data from these two spaces have found that, generally, any study related to anomaly detection can achieve the same or similar results when applied to both dimensional spaces. However, there have been no studies that compare differences in these spaces related to anomaly detection strategy based on Kittler’s Taxonomy (ADS-KT). This study aims to investigate the differences between both spaces when dimensionality reduction is associated with ADS-KT while analyzing a satellite image. Our methodology starts applying the pre-processing phase of the ADS-KT to create the high-dimensional space. Next, a dimensionality reduction technique generates the low-dimensional space. Then, we analyze extracted features from both spaces based on visualizations. Finally, machine-learning approaches, in accordance with the ADS-KT, produce results for both spaces. In the results section, metrics assessing transformed data present values close to zero contrasting with the high-dimensional space. Therefore, we conclude that dimensionality reduction directly impacts the application of the ADS-KT. Future work should investigate whether dimensionality reduction impacts the ADS-KT for any set of attributes.

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

“…Our research is applied in this context. Among all pertinent areas of research, we found our inspiration to solve this matter in remote sensing and studies related to Kittler's Taxonomy, such as [11,16,17]. This study opens up a range of possibilities for future work in remote sensing and other areas.…”

“…Lastly, by adding the two results obtained in the multiplication step, the final result was created. This step is expressed by Equations ( 15)- (17).…”

“…To define the parameter values, many of the choices were guided by the default values provided by the tools. Furthermore, previously published studies [11,16,17] helped in choosing the parameter values. The values of the parameters used in the steps of this methodology are presented in Table A1.…”

“…Investigating machine learning, pattern recognition, and artificial intelligence strategies is a continuing concern within the field of remote sensing, especially for surface water monitoring. These strategies have been successful in a variety of applications [1][2][3][4][5][6][7][8][9][10][11], including water pollution monitoring [7,8,[11][12][13][14][15][16][17]. Among these strategies, those that detect anomalies based on Kittler's [18] Taxonomy [11,16,17] are important tools to identify and categorize patterns that present unexpected behaviors.…”

“…These strategies have been successful in a variety of applications [1][2][3][4][5][6][7][8][9][10][11], including water pollution monitoring [7,8,[11][12][13][14][15][16][17]. Among these strategies, those that detect anomalies based on Kittler's [18] Taxonomy [11,16,17] are important tools to identify and categorize patterns that present unexpected behaviors. However, more studies need to address the practical applications of the Anomaly Detection Strategy based on Kittler's Taxonomy (ADS-KT), because it still faces challenges for surface water monitoring.…”