LMC: Homography Matrix-Based Local Motion Consistent Matching for Remote Sensing Images

LMC.py is the main program of the LMC method, which includes the implementation of LMC_LPM() and LMC_RANSAC() functions for LMC_LPM and LMC_RANSAC methods, respectively. LMC_LPM() takes putative matches ${(m1,m2)}$, $K$, and $\tau$ as input, and the LPM parameters use the default values. LMC_RANSAC() takes assumed matches ${(m1,m2)}$, $K$, $\tau$, and $\alpha$ as input, and all the parameters have the default values in the paper. If the input is an image instead of putative matches, SIFT() function should be used to generate the putative match set.

Qualitative Analysis.py is the program that visualizes the matching results of the LMC method in various datasets.

Test_alg_byH.py is the program that calculates the performance metrics (F-score, Recall, Precision, and Runtime) of the LMC method in the HPatches dataset.

Test_alg_byMat.py is the program that calculates the performance metrics of the LMC method in the RS, DTU, and Retina datasets.

Test_alg_bySUIRD.py is the program that calculates the performance metrics of the LMC method in the SUIRD dataset.

part_of_datasets contains some of the datasets used in the experiments. However, the DTU, HPatches, and SUIRD datasets are incomplete. For the complete datasets, please visit:

RS, DTU, Retina: https://github.com/StaRainJ/Image_matching_Datasets

HPatches: https://github.com/hpatches/hpatches-dataset (hpatches-sequences-release)

SUIRD: https://github.com/yyangynu/SUIRD