Change Detection Techniques of Remote Sensing Imageries

variety Detection Techniques of Remote Sensing Imageries1.1 Introduction all over the past years, academics demand suggested enormous accounts of replace undercover work techniques of contrasted sensing ii-base hitries and classified them from a diametrical point of views 28. These techniques depend on the assumption of spatial independence among pixels. This assumption is valid moreover for low, specialty and high- contract ranges but insufficient for VHR images 1. This chapter presents the concept, capital punishment, and discernment of seven switch over perception techniques using low, medium and high-resolution ORSI. The rest of this chapter is organized into eight pieces. member 3.2 presents a brief description of the study surface celestial orbits. separate 3.3 describes the data roundab show up characteristics of the study atomic number 18as (Sharm El-Sheikh urban center and Mahalla al-kubra city Egypt). Section 3.4 presents the pre-processing performe d on the image dataset before qualifying maculation process. Section 3.5 pass ons the accuracy assessment measures use for evaluation of the vary detective work process. Section 3.6 illustrates the concepts of the selected seven wobble detection techniques. These techniques atomic number 18 post-classification, direct multi-date classification (DMDC), image differencing (ID), image rationing (IR), image symmetric relative passing (ISRD), transport transmitter analysis ( accident), and star character differencing (PCD). Section 3.7 presents the experimental work. It explains the Implementation and accuracy assessment of applying the selected spay 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 ara through the period from 2010 to 2015. Finally, segment 3. 9 gives the chapter summary.1.2 The study beasIn this chapter, two study subject fields are selected for the application of the selected change detection techniques. The commencement ceremony area is a part of Sharm el-Sheikh city. It is located on the southern basisfill of the Sinai Peninsula, in the South Sinai Governorate, Egypt, on the coastal halt along the Red Sea as shown in ascertain (3.1).Its population 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 get up Sinai. Today the city is a holiday resort and signifi nookyt center for tourism in Egypt. The selected area is about 12.5 Km2.The second study area is a colonization 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 b ank of the Damietta Branch tributary, as shown in figure (3.2). The city is known for its textile industry. It is the largest 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 utilise. The offset of all dataset consists of two images of Sham el-Sheikh city acquired by Landsat 7 at 2000 and 2010 respectively as shown in figure (3.3). Area of the image lies amid 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 bands accomplishment dateSize 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 acqui red at 2010.Figure (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 system that could acquire Satellite images from Google Earth. The image area lies between 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 Change DetectionBefore change detection process, it is usually needed to carry out the radiometric study and image adaptation for the dataset used 64. In sec tions 3.4.1and 3.4.2, the concept of radiometric and image alteration are depict. The execution of preprocessing on the dataset used is given in section 3.7.2.1.4.1 Radiometric correctionRadiometric conditions are influenced by many featureors such as different tomography seasons or dates, different solar altitudes, different view angles, different meteorologic conditions and different cover areas of cloud, rain or atomic number 6 and so on It may affect the accuracy of most change detection techniques. Radiometric correction is performed to remove or reduce the inconsistency between the note protects examineed by sensors and the spectral reflectivity and spectral beam chic of the objects, which encompasses absolute radiometric correction and relative radiometric correction 26.Absolute radiometric correctionIt mainly rectifies the radiation distortion that is tangential to the radiation features of the object surface and is caused by the state of sensors, solar illuminat ion, and dispersion and absorption of atmospherical etc. The typical methods mainly consist of adjusting the radiation nurse 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 flock the atmospheric parameter and background signal objects of the current 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 an other(a) image to make it match with the former one. Main methods consist of correction by histogr am rule 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 channelize in the solar spectrum and so on. It should be pointed that radiometric correction isnt obligatory 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 graspable because generally spurious turn ups of change detection impart be formed if there is misregistration. If great registration accuracy isnt available, a great deal of false change area in the scene will be caused by image displacement. It is comm just now approved that the nonrepresentationalal 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 equal for all registration data sources and all detected objects and if suitable how more than it is. Another caper is w hether 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 worth 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 o r features and in this manner, the harsh prerequisite of perfect registration can be escaped 65. However, these methods neglect the key problem of the distinction between radiometric and semantic changes. So, it does not address the problem of change detection from a general perspective. It just focuses on specialised applications relevant to the end exploiter 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 reference) 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 ground substance 26. It describes the comparison or cross-tabulation of the classified land cover to the effective land cover revealed by the sample sites results in an error matrix 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 determine in an error matrix. These measures are used to evaluate the change detection process. These measures are overall accuracy, procedures accuracy and user accuracy 70.boilersuit accuracy of the change procedureIt presents 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 be 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 headd 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 pix els 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.Classified land coverActual land coverClass1 = changeClass2 = no changeClass1 = changeCorrectFalseClass2 = no changeFalseCorrect1.6 Concepts of the selected change detection techniques septette 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 near zero as shown in figure (3.5). This simple manner easily infers the resulting image conversely, it is vital to correctly describe the thresholds to perceive 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 cruciate 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 deriving of a change map that measures the proportion change in the pixel, nonetheless 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 additional 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 revelation of real change in the image than purely the absolute change 73.Change Vector abridgment 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 s pectral 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 first 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 expected 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 visualize d using a change matrix indicating, for both dates, the number of pixels in each class. This matrix allows us to interpret what changes occurred for a specic class. The main advantage of this 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 free classications (e.g., 64% nal accuracy for two 80% independent classication accuracies) 74.1.7 Experimental 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 setup obligateing methods described in section 3.5 requires a suitable setup (environment). The setup requirements are summarized in software and hardware. A laptop machine with processor Intel(R) core (TM)i7-4500U CPU 1.80 GH 2.40 GH and repulse 8 GB is used as hardware environment. ERDASD IMAGINE 2014 is selected to be the s oftware environment. It has the Model nobleman 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 close 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 aft(prenominal) 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 for 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 correspond to equation (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 erro r 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 accuracy, 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 equation (3.4).2- Threshold values were ascertain according to the statistical calculation by taking (1* STD) to identify the land cover change. This step provides a double star image for each band, 1 as change and 0 as non-change.3- The change map is produced according to the volume 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 symmetric 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- delineate the principle componentof the two images.2- Apply equation (3.8).3- Thresholds were deter mined 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