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RFCMorphologyRandomSampling.m
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RFCMorphologyRandomSampling.m
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function RFCMorphologyRandomSampling(DataFile, timeofRepeatition)
% hyperspectral classification with spectral feature using random sampling
% and nonlinear SVM
addpath('..\data\remoteData');
addpath('..\tools\libsvm-3.20\matlab');
addpath('..\tools\matlab2weka');
rawData = importdata(DataFile);% Load hyperspectral image and groud truth
if ndims(rawData) ~= 3
return;
end
indexof_= find(DataFile == '_',1);
if isempty(indexof_)
subfix = DataFile(1:end-4);
else
subfix = DataFile(1:indexof_-1);
end
resultsFile = ['Jresults\', subfix, '_', mfilename, '.mat'];
groundTruth = importdata([subfix, '_gt.mat']);
dataCube = mm(rawData);
% figure, imagesc(groundTruth);
[m, n, b] = size(dataCube);
vdataCube = reshape(dataCube, [m*n,b]);
vgroundTruth = reshape(groundTruth, [numel(groundTruth),1]);
numofClass = max(groundTruth(:));
trainingIndex = cell(numofClass,1);
testingIndex = cell(numofClass,1);
trainingSamples = cell(numofClass,1);
testingSamples = cell(numofClass,1);
trainingLabels = cell(numofClass,1);
testingLabels = cell(numofClass,1);
numofTest = zeros(numofClass,1);
sampleRateList = [0.05, 0.1, 0.25];
for repeat = 1:timeofRepeatition
for i = 1 : length(sampleRateList)
sampleRate = sampleRateList(i);
for c = 1: numofClass
cc = double(c);
class = find(vgroundTruth == c);
if isempty(class)
continue;
end
perm = randperm(numel(class));
breakpoint = round(numel(class)*sampleRate);
trainingIndex{c} = class(perm(1:breakpoint));
testingIndex{c} = class(perm(breakpoint+1:end));
trainingSamples{c} = vdataCube(trainingIndex{c},:);
trainingLabels{c} = ones(length(trainingIndex{c}),1)*cc;
testingSamples{c} = vdataCube(testingIndex{c},:);
testingLabels{c} = ones(length(testingIndex{c}),1)*cc;
numofTest(c) = numel(testingIndex{c});
end
mtrainingData = cell2mat(trainingSamples);
mtrainingLabels = cell2mat(trainingLabels);
mtrainingIndex = cell2mat(trainingIndex);
mtestingData = cell2mat(testingSamples);
mtestingLabels = cell2mat(testingLabels);
mtestingIndex = cell2mat(testingIndex);
trainingMap = zeros(m*n,1);
trainingMap(mtrainingIndex) = mtrainingLabels;
% figure, imagesc(reshape(trainingMap,[m,n])); % check the training samples
mtrainingData = double(mtrainingData);
% classification
predicted_labels = wekaClassificationWarp(mtrainingData, mtrainingLabels, mtestingData);
results(i, repeat) = assessment(mtestingLabels, predicted_labels, 'class' ); % calculate OA, kappa, AA
resultMap = vgroundTruth;
resultMap(mtestingIndex) = predicted_labels;
%figure; imagesc(reshape(resultMap,[m,n]));
end
end
save(resultsFile, 'results');