深度学习遥感影像分类_数据集批量准备
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近年来,深度学习在遥感影像地物分类中取得了一系列显著的效果。CNN可以很好的获取影像纹理信息,捕捉像素与像素之间的空间特征,因此,一个训练好的深度学习模型在地物提取中具有很大的优势。但模型的训练却是一个很繁琐的任务,需要人工准备数据集,贴标签,训练模型等。本文将以sar影像为例实现冰水二分类的数据集批量准备工作(划线取点截取小图片保存):
1.原始sar遥感影像
2.预处理思路:
a.人工划线:对应在冰和水上画n条线(自己设置,注意自己需要针对类别所占比例控制线条数量和长度)
b.保存小图片:获取直线上点坐标,以每个像素点为中心取21×21的小图片(类似mnist数据集,尺寸自己设置),保存至文件夹
c. 创建label:以保存的小图片名称+空格+类别(0或者1)将label保存至新创建的txt文档中
3.代码实现:
a.创建一个main函数调用drawTrainingSamples(img);CreateTrainSmallImages(img);drawValSamples(img);CreateValSmallImages(img);这四个函数,功能分别是和划训练集,创建训练集,划验证集,创建验证集
clear ; clc; img = imread(‘150905_multilook_4_s1a-ew-grd-hv-20150905t174712-20150905t174812-007583-00a7f0-002.tiff‘); %准备训练集数据 drawTrainingSamples(img); CreateTrainSmallImages(img); %准备验证集数据 drawValSamples(img); CreateValSmallImages(img);
b.drawTrainingSamples(img)
function [] = drawTrainingSamples(img) n_ice=4; n_water=4; h_im=imshow(img); bw_train_ice=zeros(size(img)); bw_train_water=zeros(size(img)); fprintf(‘please draw four lines on the picture for preparing the training sets of Ice‘); for i = 1:n_ice h = imline; bw = createMask(h,h_im); bw_train_ice=bw_train_ice+bw; end figure,imshow(bw_train_ice); h_im=imshow(img); fprintf(‘please draw four lines on the picture for preparing the training sets of Water‘); for i = 1:n_water h = imline; bw = createMask(h,h_im); bw_train_water=bw_train_water+bw; end figure,imshow(bw_train_water); save(‘bw_train_ice.mat‘,‘bw_train_ice‘); save(‘bw_train_water.mat‘,‘bw_train_water‘);
c.CreateTrainSmallImages(img)
function [] = CreateTrainSmallImages(img) %创建小图片 load bw_train_ice; load bw_train_water; fprintf(‘Creating training small images...‘); [X,Y]=find(bw_train_ice==1); A=[X,Y]; A; [a,b]=size(A); mkdir(‘train‘); for i=1:a m=A(i,1); n=A(i,2); SmallImage=img(m-10:m+10,n-10:n+10); imwrite(SmallImage,[‘train/‘,num2str(i),‘.jpg‘]); fid = fopen(‘train.txt‘, ‘a‘); t=[num2str(i),‘.jpg‘]; fprintf(fid, ‘%s %d \\n‘, t,0); fclose(fid); end [X,Y]=find(bw_train_water==1); B=[X,Y]; B; [a,b]=size(B); for j=1:a m=B(j,1); n=B(j,2); SmallImage=img(m-10:m+10,n-10:n+10); j=i+j; imwrite(SmallImage,[‘train/‘,num2str(j),‘.jpg‘]); fid = fopen(‘train.txt‘, ‘a‘); t=[num2str(j),‘.jpg‘]; fprintf(fid, ‘%s %d \\n‘, t,1); fclose(fid); end end
d.drawValSamples(img)
function [] = drawValSamples(img) n_ice=4; n_water=4; h_im=imshow(img); bw_val_ice=zeros(size(img)); bw_val_water=zeros(size(img)); fprintf(‘please draw four lines on the picture for preparing the validition sets of Ice‘); for i = 1:n_ice h = imline; bw = createMask(h,h_im); bw_val_ice=bw_val_ice+bw; end figure,imshow(bw_val_ice); h_im=imshow(img); fprintf(‘please draw four lines on the picture for preparing the validition sets of Water‘); for i = 1:n_water h = imline; bw = createMask(h,h_im); bw_val_water=bw_val_water+bw; end figure,imshow(bw_val_water); save(‘bw_val_ice.mat‘,‘bw_val_ice‘); save(‘bw_val_water.mat‘,‘bw_val_water‘);
e.CreateValSmallImages(img)
function [] = CreateValSmallImages(img) %创建小图片 load bw_val_ice; load bw_val_water; [X,Y]=find(bw_val_ice==1); A=[X,Y]; A; [a,b]=size(A); mkdir(‘val‘); fprintf(‘Creating validition sets small images...‘); for i=1:a m=A(i,1); n=A(i,2); SmallImage=img(m-10:m+10,n-10:n+10); imwrite(SmallImage,[‘val/‘,num2str(i),‘.jpg‘]); fid = fopen(‘val.txt‘, ‘a‘); t=[num2str(i),‘.jpg‘]; fprintf(fid, ‘%s %d \\n‘, t,0); fclose(fid); end [X,Y]=find(bw_val_water==1); B=[X,Y]; B; [a,b]=size(B); for j=1:a m=B(j,1); n=B(j,2); SmallImage=img(m-10:m+10,n-10:n+10); j=i+j; imwrite(SmallImage,[‘val/‘,num2str(j),‘.jpg‘]); fid = fopen(‘val.txt‘, ‘a‘); t=[num2str(j),‘.jpg‘]; fprintf(fid, ‘%s %d \\n‘, t,1); fclose(fid); end end
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