Real-time remote sensing detection framework of the earth's surface anomalies based on a priori knowledge base

Abstract

Due to the suddenness, diversity, randomness and complexity of various earth's surface anomalies caused by natural and human factors, the time-efficiency of traditional satellite remote sensing technology is seriously lagging behind the actual needs of the earth's surface anomalies early warning and emergency handling. Real-time earth's surface anomaly detection has become an important issue for achieving ecological security, human safety and health, and high-quality socioeconomic development. Therefore, this study proposed a real-time detection framework for earth's surface anomalies using a single temporal remote sensing data based on a priori knowledge base. The framework first selected an appropriate remote sensing feature system and constructed a priori knowledge base effectively reflecting the natural evolutionary law of the normal earth's surface. Then, the earth's surface anomalies were identified by comparing the real-time acquired remote sensing data to the priori knowledge base with the designed detection rules. Finally, the intensity of earth's surface anomalies was evaluated comprehensively using pixel- and patch-scale information. The preliminary analysis of several specific cases indicated that the proposed framework was reliable and could effectively identify the earth's surface anomalies with high accuracy. This proposed framework is fully applicable to the real-time detection of most earth's surface anomalies using a single temporal remote sensing data, which is not limited to a single earth's surface anomaly event. Meanwhile, the framework presents great potential for on-orbit processing of the earth's surface anomaly detection and realizing real-time remote sensing services for the early warning of the earth's surface anomalies.

Keywords

• Earth's surface anomalies
• Real-time remote sensing detection
• Remote sensing feature
• Priori knowledge base