Unsupervised Anomaly Detection in Single-Band or Multi-Band Images

In this document, we introduce an unsupervised anomaly detection algorithm applicable to both single-band and multi-band images. The algorithm employs advanced statistical modeling techniques such as Gaussian mixture models or local outlier factor analysis to effectively identify anomalies in image data. It features adaptive thresholding mechanisms and multidimensional feature space analysis for optimal detection accuracy. The algorithm's implementation typically involves preprocessing steps like normalization and dimensionality reduction using PCA, followed by clustering-based anomaly scoring. Key functions include automated parameter tuning and multi-scale analysis to handle varying anomaly sizes. With its superior performance, this algorithm can be effectively applied to various domains including medical image processing and environmental monitoring. By leveraging this method, users can gain deeper insights into image datasets, identify anomalous patterns, and deliver more comprehensive and accurate analytical results.