The folder stores the implementation process of Polarimetric SAR Image Factorization.
If you use this code, please refer to below papers:
[1] Feng Xu, Qian Song, and Ya-Qiu Jin, "Polarimetric SAR Image Factorization", IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 9, pp. 5026-5041, 2017.
NMF optimization using graph regularized non-negative matrix factorization with divergence formulation locality preserving.
[1] Deng Cai, Xiaofei He, Xuanhui Wang, Hujun Bao, and Jiawei Han. "Locality Preserving Nonnegative Matrix Factorization", Proc. 2009 Int. Joint Conf. on Arti_cial Intelligence (IJCAI-09), Pasadena, CA, July 2009.
[2] Deng Cai, Xiaofei He, Jiawei Han, Thomas Huang. "Graph Regularized Non-negative Matrix Factorization for Data Representation", IEEE Transactions on Pattern Analysis and Machine Intelligence, , Vol. 33, No. 8, pp. 1548-1560, 2011.
- Matlab
The datasets adopted in our paper "Polarimetric SAR Image Factorization" are collected by UAVSAR, which can be downloaded by the website https://uavsar.jpl.nasa.gov.
- C_TestData1.mat The covariance matrix of a UAVSAR PolSAR image with size 300*300.
- C_TestData1.mat The covariance matrix of a UAVSAR PolSAR image with size 900*900.
Non-negative matrix factorization is implemented using the graph-nmf algorithm by Deng Cai [code from: https://github.com/ZJULearning/MatlabFunc/tree/master/MatrixFactorization].