Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification
Author:
Affiliation:
1. School of Information Engineering, China University of Geosciences (Beijing), Beijing, China
2. Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, China
Funder
the National Natural Science Foundation of China
the National Key Research and Development Program
the Fundamental Research Funds for the Central Universities
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences
Link
https://www.tandfonline.com/doi/pdf/10.1080/01431161.2018.1513666
Reference61 articles.
1. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
2. Achanta, R., A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk. 2010. SLIC Superpixels. Technical Report 149300. EPFL.
3. Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis
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