近年來,深度學習在遙感影像地物分類中取得了一系列顯著的效果。CNN可以很好的獲取影像紋理信息,捕捉像素與像素之間的空間特征,因此,一個訓練好的深度學習模型在地物提取中具有很大的優勢。但模型的訓練卻是一個很繁瑣的任務,需要人工准備數據集,貼標簽,訓練模型等。本文將以sar影像為例實現冰水二分類的數據集批量准備工作(划線取點截取小圖片保存):
1.原始sar遙感影像
2.預處理思路:
a.人工划線:對應在冰和水上畫n條線(自己設置,注意自己需要針對類別所占比例控制線條數量和長度)
b.保存小圖片:獲取直線上點坐標,以每個像素點為中心取21×21的小圖片(類似mnist數據集,尺寸自己設置),保存至文件夾
c. 創建label:以保存的小圖片名稱+空格+類別(0或者1)將label保存至新創建的txt文檔中
3.代碼實現:
a.創建一個main函數調用drawTrainingSamples(img);CreateTrainSmallImages(img);drawValSamples(img);CreateValSmallImages(img);這四個函數,功能分別是和划訓練集,創建訓練集,划驗證集,創建驗證集
clear ;
clc;
img = imread('150905_multilook_4_s1a-ew-grd-hv-20150905t174712-20150905t174812-007583-00a7f0-002.tiff');
%准備訓練集數據
drawTrainingSamples(img);
CreateTrainSmallImages(img);
%准備驗證集數據
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)
%創建小圖片
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)
%創建小圖片
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
