Pansharpening-code.zip

net_test_fr.m: 测试自己方法在FR下的指标（D_lambda, D_s, HQNR） batch_evaluate_full_resolution.m: 测试所有方法（传统的、基于深度学习的）在FR下的指标 batch_evaluate_reduced_resolution.m: 测试所有方法（传统的、基于深度学习的）在RR下的指标 copyright (c) 2020. All rights reserved. This work should only be used for nonprofit purposes. MATLAB package for the following pansharpening algorithms: EXP: MS image interpolation using a polynomial kernel with 23 coefficients [43] BT-H [59] BDSD [33] C-BDSD [34] BDSD-PC [35] GS [24] GSA [30] C-GSA [32] PRACS [29] AWLP [61] with revised statistical matching between PAN and MS bands [31] MTF-GLP: GLP [43] with MTF-matched filter [47], unitary injection model, and revised statistical matching between PAN and MS bands [31] MTF-GLP-FS: GLP [43] with MTF-matched filter [47] with a new FS regression-based injection model [133] MTF-GLP-HPM: GLP with MTF-matched filter [47] with HPM injection model [143] and revised statistical matching between PAN and MS bands [31] MTF-GLP-HPM-H: GLP with MTF-matched filter [47] with HPM injection model [143] and haze correction [59] MTF-GLP-HPM-R: GLP with MTF-matched filter [47] and HPM injection model [143] with a new preliminary regression-based spectral matching phase [131] MTF-GLP-CBD: GLP [43] with MTF-matched filter [47] and regression-based injection model [132] C-MTF-GLP-CBD: context-based GLP [43] with MTF-matched filter [47] and regression-based injection model [132] with local parameter estimation exploiting clustering [32] MF: nonlinear decomposition scheme using MFs based on half gradient [55] FE-HPM: filter estimation based on a semiblind deconvolution framework and HPM injection model [50] SR-D: pansharpening based on sparse representation of injected details [88] PWMBF: model-based fusion using PCA and wavelets [79] TV: pansharpening based on TV [67] RR: model-based reduced-rank pansharpening [72], [134] PNN: proposed in [91] PNN-IDX: PNN augmenting the input by including several maps of nonlinear radiometric indexes as proposed in [91] A-PNN: proposed in [95] A-PNN-FT: proposed in [95] For more information and to have a look at the relative references, please refer to the paper: G. Vivone, M. Dalla Mura, A. Garzelli, R. Restaino, G. Scarpa, M.O. Ulfarsson, L. Alparone, and J. Chanussot, "A New Benchmark Based on Recent Advances in Multispectral Pansharpening: Revisiting pansharpening with classical and emerging pansharpening methods", IEEE Geoscience and Remote Sensing Magazine, doi: 10.1109/MGRS.2020.3019315. Main functions to call: "Fusion_Algorithms_Reduced_Resolution", "Fusion_Algorithms_Full_Resolution", and "Main_Reduced_Resolution" How to use: For the full resolution assessment: 1. Load the full resolution dataset (I_MS_LR is the low resolution MS image; I_PAN is the PAN image; I_MS is the upsampled MS image at PAN scale) 2. Run Fusion_Algorithms_Full_Resolution.m For the reduced resolution assessment: 1. Load the reduced resolution dataset (I_GT is the ground-truth image (high spatial resolution MS image); I_MS_LR is the low resolution MS image; I_PAN is the PAN image; I_MS is the upsampled MS image at PAN scale) NOTE: If the available dataset is at full resolution, you can obtain the reduced resolution version by properly modifying the file Main_Reduced_Resolution.m 2. Run Fusion_Algorithms_Reduced_Resolution.m System requirements: Matlab2019b or higher versions, with deep learning toolboxes. The running on previous Matlab versions is not guaranteed. Execution Environment: The code of CS, MRA, and VO approaches run on CPU. The ML code runs on GPU when available or on CPU, otherwise.