Abstract
We introduce a methodology for semiautomatic thematic map generation from remotely sensed Earth Observation raster image data based on user-selected examples. The methodology is based on a probabilistic k-nearest neighbor supervised classification algorithm. Efficient operation is attained by exploiting data structures for high-dimensional indexing. The methodology is integrated in a Web-mapping server that is coupled to an HTML supervision interface that supports interactive navigation as well as model training and tuning. Quantitative classification quality and performance measurements are extracted for real optical data with 0.25 m resolution on a highly diverse training area.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
