1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
|
/*
* Copyright (C) 2005-2022 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/* Example usage:
./CloudDetectionExample Input/CloudsOnReunion.tif \
Output/CloudDetectionOutput.tif \
Output/pretty_CloudsOnReunion.png \
Output/pretty_CloudDetectionOutput.png \
553 \
467 \
734 \
581 \
0.4 \
0.6 \
1.0
*/
// The cloud detection functor is a processing chain composed by the
// computation of a spectral angle (with SpectralAngleFunctor). The
// result is multiplied by a gaussian factor (with
// CloudEstimatorFunctor) and finally thresholded to obtain a binary
// image (with CloudDetectionFilter). However, modifications can be
// added in the pipeline to adapt to a particular situation.
//
// This example demonstrates the use of the
// \doxygen{otb}{CloudDetectionFilter}. This filter uses the spectral
// angle principle to measure the radiometric gap between a reference
// pixel and the other pixels of the image.
//
// The first step toward the use of this filter is the inclusion of
// the proper header files.
#include "otbCloudDetectionFilter.h"
#include "otbImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "otbVectorRescaleIntensityImageFilter.h"
#include "otbMultiChannelExtractROI.h"
int main(int argc, char* argv[])
{
if (argc != 12)
{
std::cerr << "Usage: " << argv[0];
std::cerr << "inputFileName outputFileName printableInputFileName printableOutputFileName";
std::cerr << "firstPixelComponent secondPixelComponent thirdPixelComponent fourthPixelComponent ";
std::cerr << "variance ";
std::cerr << "minThreshold maxThreshold " << std::endl;
return EXIT_FAILURE;
}
const unsigned int Dimension = 2;
// Then we must decide what pixel type to use for the images. We choose to do
// all the computations in double precision.
using InputPixelType = double;
using OutputPixelType = double;
// The images are defined using the pixel type and the
// dimension. Please note that the
// \doxygen{otb}{CloudDetectionFilter} needs an
// \doxygen{otb}{VectorImage} as input to handle multispectral
// images.
using VectorImageType = otb::VectorImage<InputPixelType, Dimension>;
using VectorPixelType = VectorImageType::PixelType;
using OutputImageType = otb::Image<OutputPixelType, Dimension>;
// We define the functor type that the filter will use. We use the
// \doxygen{otb}{CloudDetectionFunctor}.
using FunctorType = otb::Functor::CloudDetectionFunctor<VectorPixelType, OutputPixelType>;
// Now we can define the \doxygen{otb}{CloudDetectionFilter} that
// takes a multi-spectral image as input and produces a binary
// image.
using CloudDetectionFilterType = otb::CloudDetectionFilter<VectorImageType, OutputImageType, FunctorType>;
// An \doxygen{otb}{ImageFileReader} class is also instantiated in
// order to read image data from a file. Then, an
// \doxygen{otb}{ImageFileWriter} is instantiated in order to write
// the output image to a file.
using ReaderType = otb::ImageFileReader<VectorImageType>;
using WriterType = otb::ImageFileWriter<OutputImageType>;
// The different filters composing our pipeline are created by invoking their
// \code{New()} methods, assigning the results to smart pointers.
ReaderType::Pointer reader = ReaderType::New();
CloudDetectionFilterType::Pointer cloudDetection = CloudDetectionFilterType::New();
WriterType::Pointer writer = WriterType::New();
reader->SetFileName(argv[1]);
cloudDetection->SetInput(reader->GetOutput());
// The \doxygen{otb}{CloudDetectionFilter} needs to have a reference
// pixel corresponding to the spectral content likely to represent a
// cloud. This is done by passing a pixel to the filter. Here we
// suppose that the input image has four spectral bands.
VectorPixelType referencePixel;
referencePixel.SetSize(4);
referencePixel.Fill(0.);
referencePixel[0] = (atof(argv[5]));
referencePixel[1] = (atof(argv[6]));
referencePixel[2] = (atof(argv[7]));
referencePixel[3] = (atof(argv[8]));
cloudDetection->SetReferencePixel(referencePixel);
// We must also set the variance parameter of the filter and the
// parameter of the gaussian functor. The bigger the value, the
// more tolerant the detector will be.
cloudDetection->SetVariance(atof(argv[9]));
// The minimum and maximum thresholds are set to binarise the final result.
// These values have to be between 0 and 1.
cloudDetection->SetMinThreshold(atof(argv[10]));
cloudDetection->SetMaxThreshold(atof(argv[11]));
writer->SetFileName(argv[2]);
writer->SetInput(cloudDetection->GetOutput());
writer->Update();
// Figure~\ref{fig:CLOUDDETECTION_FILTER} shows the result of applying
// the cloud detection filter to a cloudy image.
// \begin{figure} \center
// \includegraphics[width=0.44\textwidth]{pretty_CloudsOnReunion.eps}
// \includegraphics[width=0.44\textwidth]{pretty_CloudDetectionOutput.eps}
// \itkcaption[Cloud Detection Example]{From left to right : original image, cloud mask resulting from processing.}
// \label{fig:CLOUDDETECTION_FILTER}
// \end{figure}
// Pretty image creation for printing
using OutputPrettyImageType = otb::Image<unsigned char, Dimension>;
using InputPrettyImageType = otb::VectorImage<unsigned char, Dimension>;
using WriterPrettyOutputType = otb::ImageFileWriter<OutputPrettyImageType>;
using WriterPrettyInputType = otb::ImageFileWriter<InputPrettyImageType>;
using RescalerOutputType = itk::RescaleIntensityImageFilter<OutputImageType, OutputPrettyImageType>;
using RescalerInputType = otb::VectorRescaleIntensityImageFilter<VectorImageType, InputPrettyImageType>;
using ChannelExtractorType = otb::MultiChannelExtractROI<InputPixelType, InputPixelType>;
ChannelExtractorType::Pointer selecter = ChannelExtractorType::New();
RescalerInputType::Pointer inputRescaler = RescalerInputType::New();
WriterPrettyInputType::Pointer prettyInputWriter = WriterPrettyInputType::New();
selecter->SetInput(reader->GetOutput());
selecter->SetChannel(3);
selecter->SetChannel(2);
selecter->SetChannel(1);
inputRescaler->SetInput(selecter->GetOutput());
VectorPixelType minimum, maximum;
minimum.SetSize(3);
maximum.SetSize(3);
minimum.Fill(0);
maximum.Fill(255);
inputRescaler->SetOutputMinimum(minimum);
inputRescaler->SetOutputMaximum(maximum);
prettyInputWriter->SetFileName(argv[3]);
prettyInputWriter->SetInput(inputRescaler->GetOutput());
RescalerOutputType::Pointer outputRescaler = RescalerOutputType::New();
WriterPrettyOutputType::Pointer prettyOutputWriter = WriterPrettyOutputType::New();
outputRescaler->SetInput(cloudDetection->GetOutput());
outputRescaler->SetOutputMinimum(0);
outputRescaler->SetOutputMaximum(255);
prettyOutputWriter->SetFileName(argv[4]);
prettyOutputWriter->SetInput(outputRescaler->GetOutput());
prettyInputWriter->Update();
prettyOutputWriter->Update();
return EXIT_SUCCESS;
}
|
