Change Detection Techniques of Remote Sensing Imageries

transpose Detection Techniques of Remote Sensing Imageries1.1 IntroductionOver the past years, academics have suggested enormous numbers of kind espial techniques of irrelevant sensing 2-base hitries and classified them from a antithetical point of views 28. These techniques depend on the assumption of spacial independence among pixels. This assumption is valid only for low, speciality and high-resolution icons but insufficient for VHR theatrical roles 1. This chapter presents the concept, implementation, and judgement of seven alteration maculation techniques using low, medium and high-resolution ORSI. The rest of this chapter is organized into eight surgical incisions. Section 3.2 presents a apprise description of the study neighborhoods. Section 3.3 describes the dataset characteristics of the study areas (Sharm El-Sheikh metropolis and Mahalla al-kubra city Egypt). Section 3.4 presents the pre-processing performed on the realize dataset before convince percept ion process. Section 3.5 provides the trueness sagacity measures use for evaluation of the interpolate staining process. Section 3.6 illustrates the concepts of the selected seven vary detection techniques. These techniques are post-classification, direct multi-date classification (DMDC), movie differencing (ID), image rationing (IR), image symmetric relative difference (ISRD), permute vector analysis (CVA), and principal component differencing (PCD). Section 3.7 presents the experimental work. It explains the Implementation and truth assessment of applying the selected change detection techniques on an image dataset of Sharm El-Sheikh city- Egypt. Section 3.8 presents the application of post-classification change detection technique on an image dataset of El-Mahalla El-kubra City-Egypt to detect the urban expansion over the agricultural area with the period from 2010 to 2015. Finally, section 3.9 gives the chapter summary.1.2 The study areasIn this chapter, two study areas are selected for the application of the selected change detection techniques. The first area is a dampen of Sharm el-Sheikh city. It is located on the southern landfill of the Sinai Peninsula, in the South Sinai Governorate, Egypt, on the coastal bar along the Red Sea as shown in realise (3.1).Its existence is approximately 73,000 as of 2015 62. Sharm El Sheikh is the administrative hub of Egypts South Sinai Governorate, which includes the smaller coastal towns of Dahab and Nuweiba as well as the mountainous interior, St. Catherine and Mount Sinai. Today the city is a holiday resort and significant center for tourism in Egypt. The selected area is about 12.5 Km2.The second study area is a village belongs to El Mahalla El Kubra city. El Mahalla El Kubra is a large industrial and agricultural city in Egypt, located in the middle of the Nile Delta on the western brim of the Damietta Branch tributary, as shown in figure (3.2). The city is known for its textile industry. It is the la rgest city of the Gharbia Governorate and the second largest in the Nile Delta 63. The selected area is about 38 Km2.1.3 Images datasets of the study areasIn this chapter, two datasets are used. The first dataset rests of two images of involve el-Sheikh city acquired by Landsat 7 at 2000 and 2010 respectively as shown in figure (3.3). Area of the image lies among Lat. 28 0 37.0091 N, Lon. 34 17 56.3381 E and Lat. 27 57 20.8804 N, Lon. 34 24 43.6080 E. Table (3.1) summarizes the characteristic of these images.Table (3.1 ) Characteristic of Sham el-Sheikh datasetNoSpatial resolutionRadiometric resolutionNumber of bandsAcquisition datesizing pixelsArea km2WidthHeight130 m8 bits3200038236412.5143230 m8 bits3201038236412.5143(a)(b)Fig (3.3 ) Dataset of Sharm el-Sheikh city- Egypt acquired by Landsat 7 at (a) image acquired at 2000 and the (b) image acquired at 2010. go steady (3.4) illustrates the second dataset of a village belongs to EL Mahalla al-Kubra city in Egypt. It consists of two images acquired in 2010 and 2015. It is taken by El-Shayal Smart web online Software that could acquire broadcast images from Google Earth. The image area lies mingled with Lat. 30 57 46.9032 N, Lon. 31 14 35.4776E and Lat. 30 54 47.00 N, Lon. 31 18 19.98. Table (3.2) summarizes the characteristic of this dataset.(a)(b)Fig ( 3.4 ) Dataset of EL mahalla al-kubra city- Egypt ( Google Earth) (a) image acquired at 2010 and (b) image acquired at 2015.Table (3.2 ) Characteristic of EL mahalla al-kubra datasetNoSpatial resolutionRadiometric resolutionNumber of bandsAcquisition dateSize pixelsArea km2WidthHeight16 m8 bits320101056100738.282126 m8 bits320151056100738.28211.4 Image Pre-processing for tilt DetectionBefore change detection process, it is usually necessary to carry out the radiometric subject area and image enrollment for the dataset used 64. In sections 3.4.1and 3.4.2, the concept of radiometric and image modification are described. The execution of preprocessing on the dataset used is given in section 3.7.2.1.4.1 Radiometric slumpionRadiometric conditions are influenced by more factors such as different imaging seasons or dates, different solar altitudes, different view angles, different meteorologic conditions and different bulk large areas of cloud, rain or snow etc. It whitethorn affect the truth of most change detection techniques. Radiometric correction is performed to remove or reduce the inconsistency amongst the orders surveyed by sensors and the apparitional observation and spectral radiation smarting of the purposes, which encompasses absolute radiometric correction and relative radiometric correction 26.Absolute radiometric correctionIt mainly rectifies the radiation distortion that is irrelevant to the radiation features of the inclination surface and is caused by the state of sensors, solar illumination, and dispersion and absorption of atmospheric etc. The typical methods mainly consist of adjusting the radiation value to the standard value with the transmission code of atmospheric radiation, adjusting the radiation value to the standard value with spectral curves in the lab, adjusting the radiation value to the standard value with dark object and transmission code of radiation, rectifying the scene by removing the dark objects and so on. Due to the fact that it is expensive and impractical to survey the atmospheric parameter and object objects of the period data, and almost impossible to survey that of the historical data, it is difficult to implement absolute radiometric correction in most situations in reality.Relative radiometric correctionIn a relative radiometric correction, an image is regarded as a reference image. Then adjust the radiation features of another image to make it match with the former one. Main methods consist of correction by histogram regularization and correction with fixed object. This kind of correction can remove or reduce the effects of atmosphere, sensor, and other noises. In addition, it has a simple algorithm. So it has been widely used. The radiation algorithms that are most frequently used at present in the preprocessing of change detection mainly consists of image regression method, pseudo-invariant features, dark set and bright set normalization, no-change set radiometric normalization, histogram matching, second simulation of the satellite signal in the solar spectrum and so on. It should be pointed that radiometric correction isnt necessary for all change detection methods. Although some scholars hold that radiometric corrections are necessary for multi-sensor land cover change analysis Leonardo studies at 2006 have shown that if the obtained spectral signal comes from the images to be classified, it is unnecessary to conduct atmospheric correction before the change detection of post-classification comparison. For those change detection algorithms based on feature, object comparison, radiometric correction is often unnecessary 64.1.4.2 Image registrationPrecise registration to the multi-temporal imageries is essential for numerous change detection techniques. The importance of precise spatial registration of multi-temporal imagery is understandable because generally spurious results of change detection will be formed if thither is misregistration. If great registration the true isnt available, a great deal of off-key change area in the scene will be caused by image displacement. It is commonly approved that the geometrical registration accuracy of the sub-pixel level is recognized. It can be seen that the geometrical registration accuracy of the sub-pixel level is necessary to change detection. However, it is doubtful whether this result is suitable for all registration data sources and all detected objects and if suitable how much it is. Another problem is whether this result has no influence on all change detection techniques and applications and if there is any influence how much it is. These Problems are wor th to be studied further. On the other hand, it is difficult to implement high accuracy registration between multi-temporal especially multi-sensor remote sensing images due to many factors, such as imaging models, imaging angles and conditions, curvature and rotation of the earth and so on. Especially in the mountainous region and urban area, general image registration methods are ineffective and orthorectification is needed. Although geometrical registration of high accuracy is necessary to techniques used for low, medium and high resolution (like image differencing techniques and post-classification), it is unnecessary for all change detection t. For the feature-based change detection methods like object-based change detection method, the so-called buffer detection procedure can be employed to associate the extracted objects or features and in this manner, the harsh prerequisite of perfect registration can be escaped 65. However, these methods snub the key problem of the distinc tion between radiometric and semantic changes. So, it does not address the problem of change detection from a general perspective. It just focuses on item applications relevant to the end user 1.1.5 Accuracy Assessment used for Change Detection Process evaluationThe accuracy of change detection depends on many factors, including precise geometric registration and calibration or normalization, availability and quality of ground reference data, the complexity of landscape and environment, methods or algorithms used, the analysts skills and experience, and time and cost restrictions. Authors in 66 summarized the main errors in change detection including errors in data (e.g. image resolution, accuracy of location and image quality), errors caused by pre-processing (the accuracy of geometric correction and radiometric correction), errors caused by change detection methods and processes (e.g. classification and data extraction error), errors in field survey (e.g. accuracy of ground refer ence) and errors caused by post-processing.Accuracy assessment techniques in change detection originate from those of remote sensing images classification. It is natural to extend the accuracy assessment techniques for processing single time image to that of bi-temporal or multi-temporal images. Among various assessment techniques, the most efficient and widely-used is the error hyaloplasm 26. It describes the comparison or cross-tabulation of the classified land cover to the actual land cover revealed by the sample sites results in an error intercellular substance as demonstrated in the table (3.3). It can be called a confusion matrix, contingency table 67, evaluation matrix 68 or misclassification matrix 69. Different measures and statistics can be derived from the values in an error matrix. These measures are used to evaluate the change detection process. These measures are boilersuit accuracy, procedures accuracy and user accuracy 70.Overall accuracy of the change mapIt present s the ratio of the total number of correctly classified pixels to the total number of pixels in the matrix. This figure is normally expressed as a percentage. It can be expressed as followsThe overall accuracy = (3.1)Users accuracy (column accuracy)It is a measure of the reliability of change map generated from a CD process. It is a statistic that can tell the user of the map what percentage of a class corresponds to the ground-truthed class. It is calculated by dividing the number of correct pixels for a class by the total pixels assigned to that class.The user accuracy = (3.2)Producers accuracy (raw accuracy)It is a measure of the accuracy of a particular classification scheme. It shows what percentage of a particular ground class was correctly classified. It is calculated by dividing the number of correct pixels for a class by the actual number of ground truth pixels for that class.The procedure accuracy = (3.3)Table ( 3.3 ) Change error matrix or confusion matrix.Classifie d land coverActual land coverClass1 = changeClass2 = no changeClass1 = changeCorrectFalseClass2 = no changeFalseCorrect1.6 Concepts of the selected change detection techniquesSeven LULC change detection techniques are selected to be implemented on our dataset. These techniques are post-classification, direct multi-date classification (DMDC), image differencing (ID), image rationing (IR), image symmetric relative difference (ISRD), change vector analysis (CVA), and principal component differencing (PCD).Image differencing Itis based on the subtraction of two spatially registered imageries, pixel by pixel, as followsID =Xi (t2) Xi (t1) (3.4)WhereX represents the multispectral images with I (number of bands) acquired at two different times t1and t2.The pixels of changed area are predictable to be scattered in the two ends of the histogram of the resulting image (change map), and the no changed area is grouped around zero as shown in figure (3.5). This simple manner easily infers the r esulting image conversely, it is vital to properly describe the thresholds to embrace the change from non-change regions 71.Fig (3.5 ) Histogram of the change map.Image Rationing It is similar to image differencing method. The only difference between them is the replacement of the differencing images by rationed images 71. IR =Xi (t2) / Xi (t1) (3.5)Image Symmetric Relative Difference it is based on the useof symmetric relative difference formula to measure change 72, as followsISRD = (3.6)Separating the change by the pixels value at time 1 and time 2 permits the derivation of a change map that measures the proportion change in the pixel, even so of which image is selected to be the first image. For instance, a pixel that had a value of 20 at time 1 and a value of 80 at time 2 would have an absolute change of 60, and a proportion change value in the change map of 375%(80 20) / 20 + (80-20)/80 * 100 = 375%An spare pixel with a value of 140 at time 1 and 200 at time 2 would also have an absolute change of 60, but its proportion change would only be 72.86% (200 140) / 140 + (200-140)/200 * 100 = 72.86%In general, it can be supposed that the proportion change of a pixels brightness value is more revealing of real change in the image than purely the absolute change 73.Change Vector Analysis It generates two outputs a change vector image and a magnitude image. The spectral change vector (SCV) explains the direction and magnitude of change from the first to the second date. The overall change extent per pixel is considered by defining the Euclidean distance between end points over dimensional change space, as follows (3.7)A decision on change is made based on whether the change magnitude exceeds a specific threshold. The geometric concept of CVA is applicable to any number of spectral bands 41.Principal Component Differencing It is often accepted as effective transforms to derive information and compress dimensions. Most of the information is focused on the fi rst two components. Particularly, the first component has the most information. The difference between the first principle component of two dates has the potential to advance the change detection outcomes, i.e.PCD= PC1 (X(t2)) PC1 (X(t1)) (3.8)The change detection is implemented based on threshold 28.Direct multi-date classification It combines the two images (X (t2) and X (t1)) into a single image on which a classication is performed. The areas of changes are judge to present different statistics (i. e., distinct classes) compared to the areas with no changes 74.Post-classification It is based on the classification of the two images (X (t2) and X(t1)) separately and then compared. Ideally, similar thematic classes are produced for each classication. Changes between the two dates can be visualized using a change matrix indicating, for both dates, the number of pixels in each class. This matrix allows us to generate what changes occurred for a specic class. The main advantage of t his method is the minimal impacts of radiometric and geometric differences between multi-date images. However, the accuracy of the nal result is the product of accuracies of the two independent classications (e.g., 64% nal accuracy for two 80% independent classication accuracies) 74.1.7 auditional workThis section describes the environment and the implementation procedures of seven selected change detection techniques on the first dataset of Sham el-Sheikh city.1.7.1 Experiment setupApplying methods described in section 3.5 requires a suitable setup (environment). The setup requirements are summarized in software and hardware. A laptop machine with central processor Intel(R) core (TM)i7-4500U CPU 1.80 GH 2.40 GH and RAM 8 GB is used as hardware environment. ERDASD theorize 2014 is selected to be the software environment. It has the Model maker toolbox which is used as a programming language. It is chosen for its ability to combine matrix datasets and multi-dimensional arrays that are used to represent multi-dimensional images, and also for its ability to visualize and interrogate results in an interactive manner. Moreover, it allows providing the integration of the necessary datasets and algorithmic customizations for the development of the described method.1.7.2 Pre-processingDataset of EL mahalla al-kubra described in section 3.3 had already registered before. Radiometric correction is carried out to minimize the false change detection by applying histogram matching between the two images. So, the pixel of the no changed areas in one date should take the same or finale gray level values of the corresponding pixels in the other date as shown in figure (3.6) 75.(a)(b)Fig (3.6 ) Dataset of Sharm el-Sheikh city after applying histogram matching on the image acquired at 2000 to match the image acquired at 2010.1.7.3 Implementation of the change detection techniquesThe selected techniques are implemented by the model maker in the ERDAS IMAGINE 2014 software fo r a dataset of Sharm el-Sheikhto provide an overview and assessment of LULC change detection techniques. 250 random variables are used to generate an error matrix to calculate the overall accuracy according to compare (3.1). The reference points are driven visually by comparing the two images. Table (3.4) summarizes the implementation of the selected methods.1.7.4 Results analysisThe results of applying the selected change detection techniques on the first dataset of Sham el-Sheikh city are introduced in the followingImage differencing The change map generated using the image differencing method described in section 3.6 is shown in figure (3.7). The change map has two colors. The white color represents the changed area while the black color represents the no changed area. The change error matrix is generated using 250 random variables as demonstrated in the table (3.5). The reference information is taken visually by comparing the dataset. It is used to calculate the overall accurac y, user accuracy, and the procedures accuracy. The overall accuracy of the change map is 92.4%.Table (3.4) Steps of implementation the selected change detection techniques on a dataset of Sharm el-Sheikh.MethodProceduresImage differencingID1- Applying equivalence (3.4).2- Threshold values were obdurate according to the statistical calculation by taking (1* STD) to identify the land cover change. This step provides a binary image for each band, 1 as change and 0 as non-change.3- The change map is produced according to the mass voting between the binary images.4- The overall accuracy is calculated by Producing change error matrix using 250 random variables according to equation (3.1).Image rationingIR1- Apply equation (3.5).2- Thresholds were determined as mentioned before.3- Change map is produced through majority voting between the binary images.4- The overall accuracy is calculated by Producing change error matrix using 250 random variables according to equation (3.1).Image sym metric Relative DifferenceISRD1-Apply equation (3.6)2- Thresholds were determined as mentioned before.3- Change map is produced through majority voting between the binary images.4- The overall accuracy is calculated by Producing change error matrix using 250 random variables according to equation (3.1).Change Vector analysisCVA1- Apply equation (3.7) to get the Euclidian distance between the two dates.2- Thresholds were determined as mentioned before.3- Change map is produced through majority voting between the binary images.4- The overall accuracy is calculated by Producing change error matrix using 250 random variables according to equation (3.1).Principal component differencingPCD1- Get the principle componentof the two images.2- Apply equation (3.8).3- Thresholds were determined as mentioned before.4- Change map is produced through majority voting between the binary images.5- The overall accuracy is calculated by Producing change er