matlab遥感图像分类

遥感图片的分类对于区域的分类显得尤为重要。可以分块显示，对于区域的划分显得尤其重要！

一、参考代码

clear all;close all;clc;

%读入图像

Image=imread('test.tif');

Image=Image(2:(size(Image,1)-1),2:(size(Image,2)-1),:);

Ls_Image=Linear_stretch(Image,8);%线性灰度拉伸

Im=double(Image)/255;

nums=size(Im,3);%波段数

%读入样本

snum=14;%样本数

cli_list=1:14;%各样本对应初分类别号

for i=1:snum

????eval(['sss{i}=double(imread(''s',num2str(i),'.tif''));']);%样本文件名：si.bmp,i=1,2,3...

????if size(sss{i},1)<=2 || size(sss{i},1)<=2

????????error('第%d个样本图太小！',i);

????end

????s{i}=sss{i}(2:(size(sss{i},1)-1),2:(size(sss{i},2)-1),:);

end

%初分类别各参数计算

numcli=length(unique(cli_list));%初分类别数

%当类标不是连续自然数时，修改为从1开始的连续自然数编号

cli_sort=sort(unique(cli_list));

for i=1:numcli

????ff=find(cli_list==cli_sort(i));

????clilist(ff)=i;%整理好的连续自然数类别表

end

%ELM分类

sample_s=[];

label=[];

for i=1:snum

????for j1=1:size(s{i},1)

????????for j2=1:size(s{i},2)

????????????????

????????end

????end

end

sample_s=double(sparse(sample_s))/255;

x=sample_s;

%重新编码

la_temp=label;

for i=1:ceil(log(snum)/log(2))

????y(:,i)=mod(la_temp,2);

????la_temp=floor(la_temp/2);

end

y=sign(y-0.5);

nump=size(y,1);

ELM=ELM_train(y,x,300);

%待分类原图，数据变形

Cmap=zeros(size(Im,1),size(Im,2));

for i=1:size(Im,2)

????Pclass=ELM_classify(reshape(Im(:,i,:),size(Im,1),nums),ELM);

????for j=1:1:ceil(log(snum)/log(2))

????????la_temp=(sign(Pclass)+1)*0.5;

????????Cmap(:,i)=Cmap(:,i)+la_temp(:,j)*2^(j-1);

????end

end

Cmap(find(Cmap>snum))=snum;

Cmap(find(Cmap<1))=1;

Image_cli=mat2gray(Cmap);

%制作配色表

for i=1:numcli

????map(i,1)=i;

????map(i,2)=floor(abs(i-0.5*numcli)*2);

????map(i,3)=numcli-i+1;

end

map=map./max(max(map));

Image_cli_c=grayslice(Image_cli,numcli);

%类别合并,以下内容在初次分类完成后，给定参数

?????????%1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

clf_list=[1 1 2 2 2 2 3 3 4 5 5 5 6 6];%对初分类标进行合并，各类标对应的最终类标(连续自然数);

numclf=length(unique(clf_list));

for i=1:size(Cmap,1)

????for j=1:size(Cmap,2)

????????Im_clf(i,j)=clf_list(Cmap(i,j));%最终分类矩阵

????end

end

fprintf('\n最终分类完成。');

Image_clf=mat2gray(Im_clf);

for i=1:numclf

????mapf(i,1)=i;

????mapf(i,2)=floor(abs(i-0.5*numclf)*2);

????mapf(i,3)=numclf-i+1;

end

mapf=mapf./max(max(mapf));

Image_clf_c=grayslice(Image_clf,numclf);

figure();

imshow(Image);

title('待分类原始图像');

figure();

imshow(Ls_Image)

title('待分类图像(线性拉伸)');

figure();

imshow(Image_cli_c,map);

title('初次分类结果图');

figure();

imshow(Image_clf_c,mapf);

title('最终分类结果图');

ill=ones(50,150);

illu=ill;

if(numclf>1)

for i=2:numclf

????illu=[illu;ill*i];

end

end

figure();

imshow(illu,mapf);

ylabel(' ?6 ??????????5 ??????????4 ??????????3 ??????????2 ??????????1');

title('各类颜色图例');