ABNORMAL CROWDSOURCED DATA DETECTION USING REMOTE SENSING IMAGE FEATURES

• Main Authors: G. Yu, X. Zhou, D. Hou, D. Wei
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-06-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B4-2021/215/2021/isprs-archives-XLIII-B4-2021-215-2021.pdf

Quality is the key issue for judging the usability of crowdsourcing geographic data. While due to the un-professional of volunteers and the phenomenon of malicious labeling, there are many abnormal or poor quality objects in crowdsourced data. Based on this observation, an abnormal crowdsourced data detection method is proposed in this paper based on image features. This approach includes three main steps. 1) the crowdsourced vector data is used to segment the corresponding remote sensing imagery to get image objects with a priori information (e.g., shape and category) from vector data and spectral information from the images. Then, the sampling method is designed considering the spatial distribution and topographic properties of the objects, and the initial samples are obtained, although some samples are abnormal object or poor quality. 2) A feature contribution index (FCI) is defined based on information gain to select the optimal features, a feature space outlier index (FSOI) is presented to automatically identify outlier samples and changed objects. The initial samples are refined by an iteration procedure. After the iteration, the optimal features can be determined, and the refined samples with categories can be obtained; the imagery feature space is established using the optimal features for each category. 3) The abnormal objects are identified with the refined samples by calculating the FSOI values of image objects. In order to valid the effectiveness, an abnormal crowdsourced data detection prototype is developed using Visual Studio 2013 and C# programming, the above algorithms and methods are implemented and verified using water and vegetation categories as example, the OSM (OpenStreetMap) and corresponding imagery data of Changsha city as experiment data. The angular second moment (ASM), contrast, inverse difference moment (IDM), mean, variance, difference entropy, and normalized difference green index (NDGI) of vegetation, and the IDM, difference entropy and correlation and maximum band value of water are used to detect abnormal data after the selection of image optimal feature. Experimental results show that abnormal water and vegetation data in OSM can be effectively detected in this method, and the missed detection rate of the vegetation and water are all near to zero, and the positive detection rate reach 90.4% and 83.8%, respectively.