A Pattern Analysis Image Validation Tool for the Generation of Reliable Earth Observation Image Benchmarks

Abstract

This paper describes an image validation tool for the gen-eration of good quality Earth Observation (EO) benchmark datasets. We already developed an active learning based se-mantic annotation tool which allows user fast annotate images with few samples, the tool reached about 90% accuracy. A subsequent data cleaning tool then helps correct noisy data, thus increasing the number of correctly labeled images to be qualified as benchmark data. However, this work has an an-noying bottleneck, the manual correction via visual checks still costs a considerable amount of energy. Therefore, this paper aims to introduce an image validation tool and propose new metrics to distinguish different ambiguous cases within a dataset, based on pattern analysis of the data. The interactive visualization then enables users to visualize a dataset and explore unknown patterns. The benefits are two-fold: firstly, experiments show the proposed metrics greatly help decrease the manual labor whilst keeping the essential data, thus enhancing the de-gree of automation in the process of generating good quality benchmark datasets. Secondly, our approach provides possi-bilities to interactively visualize and explore very large-scale datasets in real time, thus providing help for further data mining.