Knowledge discovery from remote sensing images: A review

Abstract The development of Earth observation (EO) technology has made the volume of remote sensing data archiving continually larger, but the knowledge hidden in massive remote sensing images has not been fully exploited. Through in‐depth research on the artificial intelligence (AI)‐based knowledge discovery approaches from remote sensing images, we divided them into four typical types according to their development stage, including rule‐based approaches, data‐driven approaches, reinforcement learning approaches, and ensemble methods. The basic principles, typical applications, advantages, and disadvantages have been detailed for commonly used algorithms within each category. Conclusions include the following: (a) Rule‐based, data‐driven and reinforcement learning algorithms form a trilogy from knowledge to data, and to capabilities. (b) Rule‐based data mining algorithms can provide prior knowledge for data‐driven approaches, the knowledge discovered by data‐driven models can be as an important complement to expert knowledge and rule sets, and reinforcement learning approaches can effectively make up for the lack of training samples or small training sample in data‐driven models. (c) The traditional data‐driven machine learning approaches and their ensemble methods are the current and may be the future mainstream methods for large regional and even global scale long time series remote sensing data mining and analysis, and improving their computing efficiency is the key research direction. (d) Deep learning, deep reinforcement learning, transfer learning, and an ensemble approach of the three may be the main means for small‐area scope, short time series, and key geoscience information extraction from remote sensing images within a long time of the future. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence