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5 Unsupervised Change Detection in Multi-Temporal SAR Images Lorenzo Bruzzone and Francesca Bovolo CONTENTS 5.1 Introduction....................................................................................................................... 107 5.2 Change Detection in Multi-Temporal Remote-Sensing Images: Literature Survey.............................................................................................................. 110 5.2.1 General Overview................................................................................................. 110 5.2.2 Change Detection in SAR Images...................................................................... 113 5.2.2.1 Preprocessing ......................................................................................... 113 5.2.2.2 Multi-Temporal Image Comparison................................................... 114 5.2.2.3 Analysis of the Ratio and Log-Ratio Image...................................... 115 5.3 Advanced Approaches to Change Detection in SAR Images: A Detail-Preserving Scale-Driven Technique............................................................... 117 5.3.1 Multi-Resolution Decomposition of the Log-Ratio Image............................. 119 5.3.2 Adaptive Scale Identification.............................................................................. 121 5.3.3 Scale-Driven Fusion ............................................................................................. 122 5.4 Experimental Results and Comparisons....................................................................... 124 5.4.1 Data Set Description............................................................................................. 124 5.4.2 Results .................................................................................................................... 126 5.5 Conclusions........................................................................................................................ 130 Acknowledgments..................................................................................................................... 131 References ................................................................................................................................... 131 5.1 Introduction The recent natural disasters (e.g., tsunami, hurricanes, eruptions, earthquakes, etc.) and the increasing amount of anthropogenic changes (e.g., due to wars, pollution, etc.) gave prominence to the topics related to environment monitoring and damage assessment. The study of environmental variations due to the time evolution of the above phenomena is of fundamental interest from a political point of view. In this context, the development of effective change-detection techniques capable of automatically identifying land-cover 107 © 2008 by Taylor & Francis Group, LLC 108 Image Processing for Remote Sensing variations occurring on the ground by analyzing multi-temporal remote-sensing images assumes an important relevance for both the scientific community and the end-users. The change-detection process considers images acquired at different times over the same geographical area of interest. These images acquired from repeat-pass satellite sensors are an effective input for addressing change-detection problems. Several different Earth-observation satellite missions are currently operative, with different kinds of sensors mounted on board (e.g., MODIS and ASTER on board NASA’s TERRA satellite, MERIS and ASAR on board ESA’s ENVISAT satellite, Hyperion on board EO-1 NASA’s satellite, SARsensorsonboardRADARSAT-1andRADARSAT-2CSA’ssatellites,IkonosandQuick-birdsatellitesthatacquireveryhighresolutionpancromaticandmulti-spectral(MS)images, etc.). Each sensor has specific properties with respect to the image acquisition mode (e.g., passiveoractive),geometrical,spectral,andradiometricresolutions,etc.Inthedevelopment of automatic change-detection techniques, it is mandatory to take into account the proper-ties of the sensors to properly extract information from the considered data. Let us discuss the main characteristics of different kinds of sensors in detail (Table 5.1 summarizes some advantages and disadvantages of different sensors for change-detection applications according to their characteristics). Images acquired from passive sensors are obtained by measuring the land-cover reflectance on the basis of the energy emitted from the sun and reflected from the ground1. Usually, the measured signal can be modeled as the desired reflectance (meas-ured as a radiance) altered from an additive Gaussian noise. This noise model enables relatively easy processing of the signal when designing data analysis techniques. Passive sensors can acquire two different kinds of images [panchromatic (PAN) images and MS images] by defining different trade-offs between geometrical and spectral resolutions according to the radiometric resolution of the adopted detectors. PAN images are char-acterized by poor spectral resolution but very high geometrical resolution, whereas MS images have medium geometrical resolution but high spectral resolution. From the perspective of change detection, PAN images should be used when the expected size of the changed area is too small for adopting MS data. For example, in the case of the analysis of changes in urban areas, where detailed urban studies should be carried out, change detection in PAN images requires the definition of techniques capable of captur-ing the richness of information present both in the spatial-context relations between neighboring pixels and in the geometrical shapes of objects. MS data should be used TABLE 5.1 Advantages and Disadvantages of Different Kinds of Sensors for Change-Detection Applications Sensor Multispectral (passive) Panchromatic (passive) SAR (active) Advantages 3 Characterization of the spectral signature of land-covers 3 The noise has an additive model 3 High geometrical resolution 3 High content of spatial-context information 3 Not affected by sunlight and atmospheric conditions Disadvantages 3 Atmospheric conditions strongly affect the acquisition phase 3 Atmospheric conditions strongly affect the acquisition phase 3 Poor characterization of the spectral signature of land-covers 3 Complexity of data preprocessing 3 Presence of multiplicative speckle noise 1Also, the emission of Earth affects the measurements in the infrared portion of the spectrum. © 2008 by Taylor & Francis Group, LLC Unsupervised Change Detection in Multi-Temporal SAR Images 109 when a medium geometrical resolution (i.e., 10–30 m) is sufficient for characterizing the size of the changed areas and a detailed modeling of the spectral signature of the land-covers is necessary for identifying the change investigated. Change-detection methods in MS images should be able to properly exploit the available MS information in the change detection process. A critical problem related to the use of passive sensors in change detection consists in the sensitivity of the image-acquisition phase to atmospheric condi-tions. This problem has two possible effects: (1) atmospheric conditions may not be conducive to measure land-cover spectral signatures, which depends on the presence of clouds; and (2) variations in illumination and atmospheric conditions at different acqui-sition times may be a potential source of errors, which should be taken into account to avoid the identification of false changes (or the missed detection of true changes). The working principle of active synthetic aperture radar (SAR) sensors is completely different from that of the passive ones and allows overcoming some of the drawbacks that affect optical images. The signal measured by active sensors is the Earth backscattering of an electromagnetic pulse emitted from the sensor itself. SAR instruments acquire different kinds of signals that result in different images: medium or high-resolution images, single-frequency or multi-frequency, and single-polarimetric or fully polarimetric images. As for optical data, the proper geometrical resolution should be chosen according to the size of the expected investigated changes. The SAR signal has different geometrical resolutions and a different penetration capability depending on the signal wavelength, which is usually included between band X and band P (i.e., between 2 and 100 cm). In other words, shorter wavelengths should be used for measuring vegetation changes and longer and more penetrating wavelengths for studying changes that have occurred on or under the terrain. All the wavelengths adopted for SAR sensors neither suffer from atmospheric and sunlight conditions nor from the presence of clouds; thus multi-temporal radar backscattering does not change with atmospheric conditions. The main problem related to the use of active sensors is the coherent nature of the SAR signal, which results in a multiplicative speckle noise that makes acquired data intrinsically complex to be analyzed. A proper handling of speckle requires both an intensive preprocessing phase and the development of effective data analysis techniques. The different properties and statistical behaviors of signals acquired by active and passive sensors require the definition of different change-detection techniques capable of properly exploiting the specific data peculiarities. In the literature, many different techniques for change detection in images acquired by passive sensors have been presented [1–8], and many applications of these techniques have been reported. This is because of both the amount of information present in MS images and the relative simplicity of data analysis, which results from the additive noise model adopted for MS data (the radiance of natural classes can be approximated with a Gaussian distribution). Less attention has been devoted to change detection in SAR images. This is explained by the intrinsic complexity of SAR data, which require both an intensive preprocessing phase and the development of effective data analysis tech-niques capable of dealing with multiplicative speckle noise. Nonetheless, in the past few years the remote-sensing community has shown more interest in the use of SAR images in change-detection problems, due to their independence from atmospheric conditions that results in excellent operational properties. The recent technological developments in sensors and satellites have resulted in the design of more sophisticated systems with increased geometrical resolution. Apart from the active or passive nature of the sensor, the very high geometrical resolution images acquired by these systems (e.g., PAN images) require the development of specific techniques capable of taking advantage of the rich-ness of the geometrical information they contain. In particular, both the high correlation © 2008 by Taylor & Francis Group, LLC 110 Image Processing for Remote Sensing between neighboring pixels and the object shapes should be considered in the design of data analysis procedures. In the above-mentioned context, two main challenging issues of particular interest in the development of automatic change-detection techniques are: (1) the definition of advanced and effective techniques for change detection in SAR images, and (2) the development of proper methods for the detection of changes in very high geometrical resolution images. A solution for these issues lies in the definition of multi-scale and multi-resolution change-detection techniques, which can properly analyze the different components of the change signal at their optimal scale2. On the one hand, the multi-scale analysis allows one to better handle the noise present in medium-resolution SAR images, resulting in the possibility of obtaining accurate change-detection maps characterized by a high spatial fidelity. On the other hand, multi-scale approaches are intrinsically suitable to exploit the information present in very high geometrical resolution images according to effective modeling (at different resolution levels) of the different objects present at the scene. According to the analysis mentioned above, after a brief survey on change detection and on unsupervised change detection in SAR images, we present, in this chapter, a novel adaptive multi-scale change detection technique for multi-temporal SAR images. This technique exploits a proper scale-driven analysis to obtain a high sensitivity to geometrical features (i.e., details and borders of changed areas are well preserved) and a high robustness to noisy speckle components in homogeneous areas. Although explicitly developed and tested for change detection in medium-resolution SAR images, this tech-nique can be easily extended to the analysis of very high geometrical resolution images. The chapter is organized into five sections. Section 5.2 defines the change-detection problem in multi-temporal remote-sensing images and focuses attention on unsupervised techniques for multi-temporal SAR images. Section 5.3 presents a multi-scale approach to change detection in multi-temporal SAR images recently developed by the authors. Section 5.4 gives an example of the application of the proposed multi-scale technique to a real multi-temporal SAR data set and compares the effectiveness of the presented method with those of standard single-scale change-detection techniques. Finally, in Section 5.5, results are discussed and conclusions are drawn. 5.2 Change Detection in Multi-Temporal Remote-Sensing Images: Literature Survey 5.2.1 General Overview A very important preliminary step in the development of a change-detection system, based on automatic or semi-automatic procedures, consists in the design of a proper phase of data collection. The phase of data collection aims at defining: (1) the kind of satellite to be used (on the basis of the repetition time and on the characteristics of the sensors mounted on-board), (2) the kind of sensor to be considered (on the basis of the desired properties of the images and of the system), (3) the end-user requirements (which are of basic importance for the development of a proper change-detection 2It is worth noting that these kinds of approaches have been successfully exploited in image classification problems [9–12]. © 2008 by Taylor & Francis Group, LLC Unsupervised Change Detection in Multi-Temporal SAR Images 111 technique), and (4) the kinds of available ancillary data (all the available information that can be used for constraining the change-detection procedure). The outputs of the data-collection phase should be used for defining the automatic change-detection technique. In the literature, many different techniques have been pro-posed. We can distinguish between two main categories: supervised and unsupervised methods [9,13]. When performing supervised change detection, in addition to the multi-temporal images, multi-temporal ground-truth information is also needed. This information is used for identifying, for each possible land-cover class, spectral signature samples for performing supervised data classification and also for explicitly identifying what kinds of land-cover transitions have taken place. Three main general approaches to supervised change detection can be found in the literature: postclassification comparison, supervised direct multi-data classification [13], and compound classification [14–16]. Postclassification comparison computes the change-detection map by comparing the classification maps obtained by classifying independently two multi-temporal remote-sensing images. On the one hand, this procedure avoids data normalization aimed at reducing atmospheric conditions, sensor differences, etc. between the two acquisitions; on the other hand, it critically depends on the accuracies of the classification maps computed at the two acquisition dates. As postclassification comparison does not take into account the dependence existing between two images of the same area acquired at two different times, the global accuracy is close to the product of the accuracies yielded at the two times [13]. Supervised direct multi-data classification [13] performs change detection by considering each possible transition (according to the available a priori information) as a class and by training a classifier to recognize the transitions. Although this method exploits the temporal correlation between images in the classification process, its major drawback is that training pixels should be related to the same points on the ground at the two times and should accurately represent the proportions of all the transitions in the whole images. Compound classification overcomes the drawbacks of supervised multi-date classifica-tion technique by removing the constraint that training pixels should be related to the same area on the ground [14–16]. In general, the approach based on supervised classifi-cation is more accurate and detailed than the unsupervised one; nevertheless, the latter approach is often preferred in real-data applications. This is due to the difficulties in collecting proper ground-truth information (necessary for supervised techniques), which is a complex, time consuming, and expensive process (in many cases this process is not consistent with the application constraints). Unsupervised change-detection techniques are based on the comparison of the spectral reflectances of multi-temporal raw images and a subsequent analysis of the comparison output. In the literature, the most widely used unsupervised change-detection techniques are based on a three-step procedure [13,17]: (1) preprocessing, (2) pixel-by-pixel compari-son of two raw images, and (3) image analysis and thresholding (Figure 5.1). The aim of the preprocessing step is to make the two considered images as comparable as possible. In general, preprocessing operations include: co-registration, radiometric and geometric corrections, and noise reduction. From the practical point of view, co-registration is a fundamental step as it allows obtaining a pair of images where corresponding pixels are associated to the same position on the ground3. Radiometric corrections reduce differences between the two acquisitions due to sunlight and atmospheric conditions. These procedures are applied to optical images, but they are not necessary for SAR 3It is worth noting that usually it is not possible to obtain a perfect alignment between temporal images. This may considerably affect the change-detection process [18]. Consequently, if the amount of residual misregistration noise is significant, proper techniques aimed at reducing its effects should be used for change detection [1,4]. © 2008 by Taylor & Francis Group, LLC ... - tailieumienphi.vn
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