深度学习遥感影像分类之数据集批量准备
深度学习遥感影像分类之数据集批量准备
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
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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);
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b.drawTrainingSamples(img)
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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' );
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c.CreateTrainSmallImages(img)
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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
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d.drawValSamples(img)
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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' );
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e.CreateValSmallImages(img)
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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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