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The all M files are for traing a Random Forest Classifier based a reference image.
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@qsly09
qsly09 authored Jun 29, 2018
1 parent c599645 commit 4dd1b6d
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21 changes: 21 additions & 0 deletions autoPara.m
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function [nrows,ncols,nbands,jiUL,resolu,zc,num_imgs] = autoPara(imf)
% Matlab code for read paramters of files automatically
% Inputs:
% imf: folder names from dir

% number of images
num_imgs = size(imf,1);
% filter for Landsat folders
imf_path = imf.folder;
imf = regexpi({imf.name}, 'L(T5|T4|E7|C8|ND)(\w*)', 'match');
imf = [imf{:}];
imf = vertcat(imf{:});
% name of the first stacked image
filename = dir(fullfile(imf_path(1,:),imf(1,:),'L*stack'));
% read in dimension and zone number of the data
[jiDim,jiUL,resolu,zc,nbands] = envihdrread(fullfile(imf_path(1,:),imf(1,:),filename.name));
% number of nrows processed
nrows = jiDim(2);
% number of pixels procesed per line
ncols = jiDim(1);
end
270 changes: 270 additions & 0 deletions autoTrainRandomForestClassifier.m
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function autoTrainRandomForestClassifier(varargin)
%AUTOTRAINRANDOMFORESTCLASSIFIER trains a RandomForestClassifier (RFC)
%based on CCDC change detection results and an land cover reference image.
%
% Specific parameters
% ------------------------
% 'SampleImage' A reference image with '.hdr' format. *REQUIRED.
% 'SampleYear' The year of the reference data. *REQUIRED.
% 'SampleNumber' Total number of training samples. Default is
% 20,000
% 'InputDirectory' Directory of CCDC change detection results.
% Default is the path to current folder.
% 'OutputDirectory' Directory of the model output. Default is the
% same as InputDirectory.
%
% History
% ------------------------
% CCDC 1.3 version - Zhe Zhu, EROS, USGS
%
% Revisions: $ Date: 11/25/2015 $ Copyright: Zhe Zhu
% Version 1.3 Select sample based on best strategy (06/30/2015)
% Version 1.2 Only use undisturbed data (05/11/2015)
% Version 1.1 Use version 7.3 for storing RF model (01/10/2015)
% Version 1.0 Fixed a bug in picking the wrong pixel for training (11/08/2014)
%
% Author: Zhe Zhu (zhe.zhu#ttu.edu)
% Shi Qiu (shi.qiu#ttu.edu)
% Date: 28. Jun, 2018

%% get parameters from inputs
% defaults
% version of CCDC
ccdc_v = 1.3;
% where the all CCDC change detection results are
dir_cur = pwd;
% where the output files are
dir_out = '';
% request the user's inputs
p = inputParser;
p.FunctionName = 'trainParas';
% optional
% default values.
addParameter(p,'InputDirectory',dir_cur);
addParameter(p,'OutputDirectory',dir_out);
addParameter(p,'SampleImage','');
addParameter(p,'SampleYear','');
addParameter(p,'SampleNumber','');

parse(p,varargin{:});
dir_cur = p.Results.InputDirectory;
dir_out = p.Results.OutputDirectory;
if isempty(dir_out)
dir_out = dir_cur;
end
ccdc_dir = dir_cur; clear dir_cur;
name_roi = p.Results.SampleImage;

if isempty(name_roi)
fprintf('Please input a reference image''s path\r\n');
end

trn_year = p.Results.SampleYear;
if isempty(trn_year)
fprintf('Please input the year the reference data presents \r\n');
end
num_tot = p.Results.SampleNumber;
if isempty(num_tot)
num_tot = 20000;
fprintf('Total number of samples are %d (default) \r\n',num_tot);
else
fprintf('Total number of samples are %d \r\n',num_tot);
end

%% Prepare for the inputs
% get image parameters automatically
imf=dir(fullfile(ccdc_dir,'L*')); % folder names
[nrows,ncols,nbands] = autoPara(imf);

% 2. ground truth time interval
gt_start = datenum(trn_year,1,1);
gt_end = datenum(trn_year,12,31);

% Constants:
% number of coefficients
num_c = 8;
jiDim = [ncols,nrows];

% use the Trends

im_roi = enviread(name_roi); % Land Cover Trends data (all pixels)

% transformed to Matlab dimension (opposite in x & y)
tim_roi = im_roi';
clear im_roi;

% Find nonzero ids with 1 2 3
% 4 5 6
idsfind = find(tim_roi > 0);
[~,i_ids] = ind2sub(jiDim,idsfind);

% length of roi pixels
rec_l = length(idsfind);
% rec_l = 1000;
% Get Training data prepared
% the training data is NxD and labels are Nx1, where N=#of
% examples, D=# of features
% intialize maximum Xs and Ys
X = zeros(rec_l,(num_c+1)*(nbands-1)); % 7 bands cft & rmse
Y = zeros(rec_l,1); % Trends classes + location

% Get into the TSFitMap folder
name_rst = 'TSFitMap';
tsfitmap_path = fullfile(ccdc_dir,name_rst);
% cd(v_input.name_rst);
% intiate i_row
i_row = -1;
% number of pixels for traning
plusid = 0;

for i=1:rec_l
% Just load once for a line of rec_cg for all reference within this line
if i_ids(i) ~= i_row

fprintf('Processing the %dth line ...\n',i);

% load CCDCRec
load(fullfile(tsfitmap_path,['record_change',num2str(i_ids(i))]));

% matrix of each component
t_start = [rec_cg.t_start];
t_end = [rec_cg.t_end];
coefs = [rec_cg.coefs];
rmse = [rec_cg.rmse];
pos = [rec_cg.pos];
categ = [rec_cg.category];
% reshape coefs
coefs = reshape(coefs,num_c,nbands-1,[]);
end

% find the curve within a fixed time interval
ids_line = find(pos == idsfind(i));

for j = 1:length(ids_line)
% id of reference data
id_ref = ids_line(j);
% position of reference data
pos_ref = pos(id_ref);

% take curves that fall witin the training period
% remove curves that are changed within training period
if t_start(id_ref) < gt_start & t_end(id_ref) > gt_end
% number of time series model for training
plusid = plusid + 1;

% two way of overall reflectance
tmp_cft = coefs(:,:,id_ref);
% temporal overall ref
% tmp_cft(1,:) = tmp_cft(1,:)+gt_mid*tmp_cft(2,:);
tmp_cft(1,:) = tmp_cft(1,:) + 0.5*(t_start(id_ref)+t_end(id_ref))*tmp_cft(2,:);

% all rmse
tmp_rmse = rmse(:,id_ref);

% prepare Xs
X(plusid,:) = [tmp_rmse;tmp_cft(:)];
Y(plusid) = tim_roi(pos_ref); % class category
end
end
i_row = i_ids(i);
end

% remove out of boundary or changed pixels
X = X(1:plusid,:);
Y = Y(1:plusid);

% make sure they are double!
Y = double(Y);
X = double(X);

% % % cd to the images folder
% % cd(l_dir);

%% selecting pixels for training
% number of variables
x_dim = size(X,2);

% update class number
all_class = unique(Y);
% update number of class
n_class = length(all_class);
% calculate proportion based # for each class
prct = hist(Y(:,1),all_class);
prct = prct/sum(prct);

% number of reference for euqal number training
eq_num = num_tot; % total #
n_min = round(0.03*num_tot); % minimum #
n_max = round(0.4*num_tot); % maximum #

% intialized selected X & Y training data
sel_X_trn = [];
sel_Y_trn = [];

for i_class = 1:n_class
% find ids for each land cover class
ids = find(Y == all_class(i_class));
% total # of reference pixels for permute
tmp_N = length(ids);

% random permute the reference pixels
tmp_rv = randperm(tmp_N);

% adjust num_prop based on proportion
adj_num = ceil(eq_num*prct(i_class));

% adjust num_prop based on min and max
if adj_num < n_min
adj_num = n_min;
elseif adj_num > n_max
adj_num = n_max;
end

if tmp_N > adj_num
% if tmp_N > adj_num, use adj_num, otherwise, use tmp_N
tot_n = adj_num;
else
tot_n = tmp_N;
end

% permutted ids
rnd_ids = ids(tmp_rv(1:tot_n));

% X_trn and Y_trn
sel_X_trn = [sel_X_trn; X(rnd_ids,:)];
sel_Y_trn = [sel_Y_trn; Y(rnd_ids)];
end

% log for CCDC Train paramters and versions
% report only for the first task
class_value = unique(sel_Y_trn);
class_number = hist(sel_Y_trn,class_value);


fileID = fopen(fullfile(ccdc_dir, 'CCDC_Train_log.txt'),'w');
% write location of image stack
fprintf(fileID,'Image location = %s\r\n',ccdc_dir);
% write number of images used
fprintf(fileID,'Number of sample = %d\r\n',sum(class_number));
% write number of images used
fprintf(fileID,'Minimum number of sample per class = %d\r\n',min(class_number));
% write number of images used
fprintf(fileID,'Maximum number of sample per class = %d\r\n',max(class_number));
% CCDC Version
fprintf(fileID,'CCDC Train Version = %.2f\r\n',ccdc_v);
% updates
fprintf(fileID,'******************************************************************************************************\r\n');
fprintf(fileID,'Revisions: $ Date: 11/20/2015 $ Copyright: Zhe Zhu\r\n');
fprintf(fileID,'Version 1.3 Select sample based on best strategy (06/30/2015)\r\n');
fprintf(fileID,'Version 1.2 Only use undisturbed data (05/11/2015)\r\n');
fprintf(fileID,'Version 1.1 Use version 7.3 for storing RF model (01/10/2015) \r\n');
fprintf(fileID,'Version 1.0 Fixed a bug in picking the wrong pixel for training (11/08/2014)\r\n');
fprintf(fileID,'******************************************************************************************************\r\n');
fclose(fileID);

modelRF = classRF_train(sel_X_trn,sel_Y_trn);
save(fullfile(dir_out,'modelRF'),'modelRF','-v7.3');

fprintf('Finishined Training! The model can be found at %s\r\n',dir_out);
end

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