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The extent of Arctic sea ice has decreased over the years and the importance of sea ice monitoring is expected to increase. Remote sensing change detection compares images acquired over the same geographic area at different times in order to identify changes that might have occurred in the area of interest. Change detection methods have been developed for cryospheric topics. The Kittler-Illingworth thresholding algorithm has proven to be an effective change detection tool, but has not been used for sea ice. Here it is applied to Arctic sea ice data. The objective is to investigate the unsupervised detection of changes in Arctic sea ice using multi-temporal SAR images. The well-known Kittler-Illingworth algorithm is tested using two density function models, i.e., the generalized Gaussian and the log-normal model. The difference image is obtained using the modified ratio operator. The histogram of the change image, which approximates its probability distribution, is considered to be a combination of two classes, i.e., the changed and unchanged classes. Histogram fitting techniques are used to estimate the unknown density functions and the prior probabilities. The optimum threshold is selected using a criterion function directly related to classification error. In this thesis three datasets were used covering parts of the Beaufort Sea from the years 1992, 2002, 2007 and 2009. The SAR and ASAR C-band data came from satellites ERS and ENVISAT respectively. All three were interpreted visually. For all three datasets, the generalized Gaussian detected a lot of change, whereas the log-normal detected less. Only one small subset of a dataset was validated against reference data. The log-normal distribution then obtained 0% false alarm rate through all trials. The generalized Gaussian obtained false alarm rates around 4% for most of the trials. The generalized Gaussian achieved detection accuracies around 95%, whereas the log-normal achieved detection accuracies around 70%. The overall accuracies for the generalized Gaussian were about 95% in most trials. The log-normal achieved overall accuracies at around 85%. The KHAT for the generalized Gaussian was in the range of 0.66-0.93. The KHAT for log-normal was in the range of 0.68-0.77. Using one additional speckle filter iteration increased the accuracy for the log-normal distribution. Generally, the detection of positive change has been accomplished with higher level of accuracy compared with negative change detection. A visual inspection shows that the generalized Gaussian distribution probably over-estimates the change. The log-normal distribution consistently detects less change than the generalized Gaussian. Lack of validation data made validation of the results difficult. The performed validation might not be reliable since the available validation data was only SAR imagery and differentiating change and no-change is difficult in the area. Further due to the lack of reference data it could not be decided, with certainty, which distribution performed the best. / Ytan av arktisk havsis har minskat genom åren och vikten av havsisövervakning förväntas öka. Förändrigsdetection jämför bilder från samma geografiska område från olika tidpunkter föra att identifiera förändringar som kan ha skett i intresseområdet. Förändringsdekteringsmetoder har utvecklats för kryosfäriska ämnen. Tröskelvärdesbestämning med Kittler-Illingworth algoritmen har visats sig vara ett effektivt verktyg för förändringsdetektion, men har inte änvänts på havsis. Här appliceras algoritmen på arktisk havsis. Målet är att undersökra oövervakad förändringsdetektion i arktisk havsis med multitemporella SAR bilder. Den välkända Kittler-Illingworth algoritmen testas med två täthetsfunktioner, nämligen generaliserad normaldistribution och log-normal distributionen. Differensbilden erhålls genom den modifierad ratio-operator. Histogrammet från förändringsbilden skattar dess täthetsfunktion, vilken anses vara en kombination av två klasser, förändring- och ickeförändringsklasser. Histogrampassningstekniker används för att uppskatta de okända täthetsfunktionerna och a priori sannolikheterna. Det optimala tröskelvärdet väljs genom en kriterionfunktion som är direkt relaterad till klassifikationsfel. I detta examensarbete användes tre dataset som täcker delar av Beaufort-havet från åren 1992, 2002, 2007 och 2009. SAR C-band data kom från satelliten ERS och ASAR C-band data kom från satelliten ENVISAT. Alla tre tolkades visuellt och för alla tre detekterade generaliserad normaldistribution mycket mer förändring än lognormal distributionen. Bara en mindre del av ett dataset validerades mot referensdata. Lognormal distributionen erhöll då 0% falska alarm i alla försök. Generalised normaldistributionen erhöll runt 4% falska alarm i de flesta försöken. Generaliserad normaldistributionen nådde detekteringsnoggrannhet runt 95% medan lognormal distributionen nådde runt 70%. Generell noggrannheten för generaliserad normaldistributionen var runt 95% i flesta försöken. För lognormal distributionen nåddes en generell noggrannhet runt 85%. KHAT koefficienten för generaliserad normaldistributionen var i intervallet 0.66-0.93. För lognormal distributionen var den i intervallet 0.68-0.77. Med en extra speckle-filtrering ökades nogranneheten för lognormal distributionen. Generellt sett, detekterades positiv förändring med högre nivå av noggrannhet än negativ förändring. Visuell inspektion visar att generaliserad normaldistribution troligen överskattar förändringen. Lognormal distributionen detekterar konsistent mindre förändring än generaliserad normaldistributionen. Bristen på referensdata gjorde valideringen av resultaten svårt. Den utförda valideringen är kanske inte så trovärdig, eftersom den tillgänliga referensdatan var bara SAR bilder och att särskilja förändring och ickeförändring är svårt i området. Vidare, på grund av bristen på referensdata, kunde det inte bestämmas med säkerhet vilken distribution som var bäst.

Multitemporal SAR images have been increasingly used for the detection of different types of environmental changes. The detection of urban changes using SAR images is complicated due to the complex mixture of the urban environment and the special characteristics of SAR images, for example, the existence of speckle. This thesis investigates urban change detection using multitemporal SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate effective methods for reduction of the speckle effect in change detection, (3) to investigate spatio-contextual change detection, (4) to investigate object-based unsupervised change detection, and (5) to investigate a new technique for object-based change image generation. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR and ENVISAT ASAR sensors were used for pixel-based change detection. For the object-based approaches, TerraSAR-X images were used. In Paper I, the unsupervised detection of urban change was investigated using the Kittler-Illingworth algorithm. A modified ratio operator that combines positive and negative changes was used to construct the change image. Four density function models were tested and compared. Among them, the log-normal and Nakagami ratio models achieved the best results. Despite the good performance of the algorithm, the obtained results suffer from the loss of fine geometric detail in general. This was a consequence of the use of local adaptive filters for speckle suppression. Paper II addresses this problem using the nonlocal means (NLM) denoising algorithm for speckle suppression and detail preservation. In this algorithm, denoising was achieved through a moving weighted average. The weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, principle component analysis (PCA) was used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the number of significant PCA components to be retained for weights computation and the required noise variance were proposed. The experimental results showed that the NLM algorithm successfully suppressed speckle effects, while preserving fine geometric detail in the scene. The analysis also indicates that filtering the change image instead of the individual SAR images was effective in terms of the quality of the results and the time needed to carry out the computation. The Markov random field (MRF) change detection algorithm showed limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle. To overcome this problem, Paper III utilizes the NLM theory to define a nonlocal constraint on pixels class-labels. The iterated conditional mode (ICM) scheme for the optimization of the MRF criterion function is extended to include a new step that maximizes the nonlocal probability model. Compared with the traditional MRF algorithm, the experimental results showed that the proposed algorithm was superior in preserving fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map. Paper IV investigates object-based unsupervised change detection using very high resolution TerraSAR-X images over urban areas. Three algorithms, i.e., Kittler-Illingworth, Otsu, and outlier detection, were tested and compared. The multitemporal images were segmented using multidate segmentation strategy. The analysis reveals that the three algorithms achieved similar accuracies. The achieved accuracies were very close to the maximum possible, given the modified ratio image as an input. This maximum, however, was not very high. This was attributed, partially, to the low capacity of the modified ratio image to accentuate the difference between changed and unchanged areas. Consequently, Paper V proposes a new object-based change image generation technique. The strong intensity variations associated with high resolution and speckle effects render object mean intensity unreliable feature. The modified ratio image is, therefore, less efficient in emphasizing the contrast between the classes. An alternative representation of the change data was proposed. To measure the intensity of change at the object in isolation of disturbances caused by strong intensity variations and speckle effects, two techniques based on the Fourier transform and the Wavelet transform of the change signal were developed. Qualitative and quantitative analyses of the result show that improved change detection accuracies can be obtained by classifying the proposed change variables. / <p>QC 20150529</p>

Multitemporal SAR images have been used successfully for the detection of different types of environmental changes. The detection of urban change using SAR images is complicated due to the special characteristics of SAR images—for example, the existence of speckle and the complex mixture of the urban environment. This thesis investigates the detection of urban changes using SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate reduction of the speckle effect and (3) to investigate spatio-contextual change detection. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR (1998~1999) and Envisat ASAR (2008~2009) sensors were used to detect changes that have occurred in these cities. Unsupervised change detection using SAR images is investigated using the Kittler-Illingworth algorithm. The problem associated with the diversity of urban changes—namely, more than one typology of change—is addressed using the modified ratio operator. This operator clusters both positive and negative changes on one side of the change-image histogram. To model the statistics of the changed and the unchanged classes, four different probability density functions were tested. The analysis indicates that the quality of the resulting change map will strongly depends on the density model chosen. The analysis also suggests that use of a local adaptive filter (e.g., enhanced Lee) removes fine geometric details from the scene. Speckle suppression and geometric detail preservation in SAR-based change detection, are addressed using the nonlocal means (NLM) algorithm. In this algorithm, denoising is achieved through a weighted averaging process, in which the weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, the PCA technique is used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the dimensionality of the new space and the required noise variance are proposed. The experimental results show that the NLM algorithm outperformed traditional local adaptive filters (e.g., enhanced Lee) in eliminating the effect of speckle and in maintaining the geometric structures in the scene. The analysis also indicates that filtering the change variable instead of the individual SAR images is effective in terms of both the quality of the results and the time needed to carry out the computation. The third research focuses on the application of Markov random field (MRF) in change detection using SAR images. The MRF-based change detection algorithm shows limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle noise. This problem has been addressed through the introduction of a global constraint on the pixels’ class labels. Based on NLM theory, a global probability model is developed. The iterated conditional mode (ICM) scheme for the optimization of the MAP-MRF criterion function is extended to include a step that forces the maximization of the global probability model. The experimental results show that the proposed algorithm is better at preserving the fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map compared with traditional MRF-based change detection algorithm. / <p>QC 20130610</p>