Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Object-based change detection using correlation image analysis and image segmentation
International Journal of Remote Sensing
Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications
Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Machine learning approaches for high-resolution urban land cover classification: a comparative study
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
High-Resolution Urban Image Classification Using Extended Features
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
A Data Mining Framework for Monitoring Nuclear Facilities
ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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As the spatial resolution of satellite remote sensing imagery is advancing towards sub meter, the predominantly pixel based (or single instance) classification methods needs be redesigned to take advantage of the spatial and structural patterns found in the very high resolution imagery. In this work, we look at the advantages of object based image analysis methods through the newer multiple instance learning learning schemes. We analyze these methods in the context of big geospatial data and allude readers to some of the outstanding computational challenges.