An Interactive Strategy for the Training Set Definition Based on Active Self-Paced Learning Implemented on a Cloud-Computing Platform

Abstract

Supervised classification of remote sensing data requires a large number of high-quality annotated samples. At the operational level, the definition of a large training set by photograph interpretation is costly and time-consuming. The manual annotation activity is typically supported by high-resolution satellite data. Therefore, when working at country or continental scale, it is necessary to efficiently access large archives of remotely sensed data. To address these issues, this letter presents an interactive strategy implemented in a cloud-computing platform for defining effective training sets with significantly reduced human effort. This is achieved by combining active learning (AL) and self-paced learning (SPL) techniques. First, an initial training set is used to classify the pool of unlabeled samples. Then, the method progressively adds high-confidence samples, selected through an SPL strategy, and low-confidence samples selected considering an AL strategy. While the high-confidence sample labels are self-paced, the low-confidence ones are manually assigned. The cloud-computing platform allows the: 1) definition of a complete training set in a fast and efficient way and 2) access to a multipetabyte catalog of satellite imagery. Experiments carried out on the Google Earth Engine (GEE) Platform demonstrate the effectiveness of the proposed strategy compared to the standard manual annotation.