matlabfcm函数代码-DPPCANet:基于深度差分图像和PCANet的鲁棒不平衡SAR图像变化检测方法

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matlab fcm函数代码DPPCA网 介绍 DPPCANet是一种用于不平衡多时相合成Kong径雷达图像变化检测的鲁棒深度学习方法,主要包括:1)生成差异图; 2)并行FCM聚类,以提供训练样本伪标签; 3)基于采样的PCANet + SVM模型构建过采样和欠采样的像素分类。 要求 MATLAB 2018a 功能 加权池卷积: I_wp = WP(I,k) 生成对数比图像: I_lr = di_gen(I_1,I_2) 累积加权池: T是累积时间 M = Normalized(matrix)是一种鲁棒的归一化方法。 输入矩阵中值最高的50个元素的平均值是上限,而值最低的50个元素的平均值是下限。 矩阵是软归一化的。 DDI = Normalized(CWP(I_ori,T)) Gabor小波变换特征提取: [f1,f_1] = Gabor_fea(I_map) 并行聚类:两组映射的DDI I_map1,I_map2和Gabor特征向量f_1和f_2 im_lab = paralleclustering(f_1,I_map1,f_2,I_map2) FCM: [center, U,

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( 45 个子文件 550KB ) matlabfcm函数代码-DPPCANet:基于深度差分图像和PCANet的鲁棒不平衡SAR图像变化检测方法
DPPCANet-master
PCANet_SVM_train.m 2.15KB
sigmoid.m 299B
.gitattributes 66B
Utils
im2colstep.mexw64 9.00KB
PCANet_train.m 2.92KB
PCA_FilterBank.m 1.68KB
HashingHist.m 2.87KB
im2col_general.m 378B
PCANet_FeaExt.m 1.76KB
im2colstep.mexa64 9.69KB
PCA_output.m 1.65KB
im2colstep.m 1.31KB
mat2imgcell.m 297B
di_gen.p 749B
PCANet_SVM_test.m 634B
main.m 1.72KB
Normalized.m 367B
WP.m 898B
pic
B2.tif 472.07KB
BGT.tif 3.97KB
B1.tif 472.07KB
GaborKernelWave.m 1.61KB
README.md 2.30KB
Liblinear
libsvmread.mexw64 10.50KB
predict.mexw64 16.00KB
train.mexw64 57.50KB
predict.mexa64 65.28KB
libsvmwrite.mexw64 9.00KB
libsvmwrite.c 2.10KB
train.mexa64 66.67KB
libsvmread.mexa64 10.96KB
libsvmwrite.mexa64 9.24KB
predict.c 8.10KB
Makefile 1.72KB
libsvmread.c 3.92KB
linear_model_matlab.c 3.47KB
linear_model_matlab.h 166B
README 7.18KB
make.m 889B
train.c 10.28KB
CWP.m 266B
parallelclustering.m 1.35KB
PE.m 883B
Gabor_fea.m 1.87KB
DDIMAP.m 422B
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