The Study of Anomaly Detection in Patterns from Remotely Sensed Images

Abstract

Anomaly detection in remotely sensed images is a critical task with diverse applications, ranging from environmental monitoring to smart agriculture. Various methodologies have been developed to enhance the detection of anomalies, which are deviations from expected patterns in image data. These methods leverage advanced computational techniques and machine learning models to improve accuracy and efficiency. Anomaly detection in remotely sensed images can be employed using different methods such as heterogeneous and edge computing, convolutional neural Networks, multi-dimensional feature space, unified anomaly detection, unsupervised learning for burnt area detection, etc. This paper discussed different methods and cutting-edge technologies for anomaly detection. While all these methods show significant advancements, challenges, limitations remain in terms of computational resource requirements and the need for real-time processing capabilities. Future research may focus on optimizing these models for broader applications and improving their adaptability to new data sources.