Unsupervised multi-scale change detection from SAR imagery for monitoring natural and anthropogenic disasters

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Radar remote sensing can play a critical role in operational monitoring of natural and anthropogenic disasters. Despite its all-weather capabilities, and its high performance in mapping, and monitoring of change, the application of radar remote sensing in operational monitoring activities has been limited. This has largely been due to: (1) the historically high costs associated with obtaining radar data; (2) slow data processing, and delivery procedures; and (3) the limited temporal sampling that was provided by spaceborne radar-based satellites. Recent advances in the capabilities of spaceborne Synthetic Aperture Radar (SAR) sensors have developed an environment that now allows for SAR to make significant contributions to disaster monitoring. New SAR processing strategies that can take full advantage of these new sensor capabilities are currently being developed. Hence, with this PhD dissertation, I aim to: (i) investigate unsupervised change detection techniques that can reliably extract signatures from time series of SAR images, and provide the necessary flexibility for application to a variety of natural, and anthropogenic hazard situations; (ii) investigate effective methods to reduce the effects of speckle and other noise on change detection performance; (iii) automate change detection algorithms using probabilistic Bayesian inferencing; and (iv) ensure that the developed technology is applicable to current, and future SAR sensors to maximize temporal sampling of a hazardous event. This is achieved by developing new algorithms that rely on image amplitude information only, the sole image parameter that is available for every single SAR acquisition. The motivation and implementation of the change detection concept are described in detail in Chapter 3. In the same chapter, I demonstrated the technique's performance using synthetic data as well as a real-data application to map wildfire progression. I applied Radiometric Terrain Correction (RTC) to the data to increase the sampling frequency, while the developed multiscaledriven approach reliably identified changes embedded in largely stationary background scenes. With this technique, I was able to identify the extent of burn scars with high accuracy. I further applied the application of the change detection technology to oil spill mapping. The analysis highlights that the approach described in Chapter 3 can be applied to this drastically different change detection problem with only little modification. While the core of the change detection technique remained unchanged, I made modifications to the pre-processing step to enable change detection from scenes of continuously varying background. I introduced the Lipschitz regularity (LR) transformation as a technique to normalize the typically dynamic ocean surface, facilitating high performance oil spill detection independent of environmental conditions during image acquisition. For instance, I showed that LR processing reduces the sensitivity of change detection performance to variations in surface winds, which is a known limitation in oil spill detection from SAR. Finally, I applied the change detection technique to aufeis flood mapping along the Sagavanirktok River. Due to the complex nature of aufeis flooded areas, I substituted the resolution-preserving speckle filter used in Chapter 3 with curvelet filters. In addition to validating the performance of the change detection results, I also provide evidence of the wealth of information that can be extracted about aufeis flooding events once a time series of change detection information was extracted from SAR imagery. A summary of the developed change detection techniques is conducted and suggested future work is presented in Chapter 6

Pygomelia and True Hermaphroditism in a Nine Week Old Large White Piglet Case Report

A nine weeks old female Large White piglet which was presented to the Veterinary Teaching Hospital, Federal University of Agriculture, Abeokuta, with a complaint of extra limbs was diagnosed with pygomelia and concurrent true hermaphroditism based on gross morphologic features, radiography, exploratory laparotomy and histopathology of the malformed organs. The piglet had two well-developed extra hind limbs consisting of the femur, tibia, fibula and the phalanges. Radiographically, the accessory limbs were attached to the ischium through a rudimentary pelvic bone. The supernumerary limbs were smaller than the normal appendages, but contained equal digits. The anal orifice was observed cranial to the right supernumerary limb. Caudal to the left supernumerary limb a rudimentary penis was observed. Two oval shaped fibrous masses were palpated in the inguinal canal of the piglet. In addition, there was a transparent tubular tract measuring 24 cm in length which contained serous fluid. The right kidney was rudimentary measuring 2.10 cm, while the left kidney appeared hypertrophied measuring 6.10 cm. The histology of the left kidney showed dysplastic areas of undifferentiated mesenchymal stroma in the cortex and medulla with the presence of groups of immature glomeruli in the cortex. The tubules in the medulla were scanty in number and had atypical epithelium. The adrenal glands had normal architecture with ectopic adrenal tissue in the adrenal capsule, while the ovaries and uterus were normal. It was concluded that the complex anomalies in the piglet might be as a result of a complex mode of inheritance

Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach

Despite the significant progress that was achieved throughout the recent years, to this day, automatic change detection and classification from synthetic aperture radar (SAR) images remains a difficult task. This is, in large part, due to (a) the high level of speckle noise that is inherent to SAR data; (b) the complex scattering response of SAR even for rather homogeneous targets; (c) the low temporal sampling that is often achieved with SAR systems, since sequential images do not always have the same radar geometry (incident angle, orbit path, etc.); and (d) the typically limited performance of SAR in delineating the exact boundary of changed regions. With this paper we present a promising change detection method that utilizes SAR images and provides solutions for these previously mentioned difficulties. We will show that the presented approach enables automatic and high-performance change detection across a wide range of spatial scales (resolution levels). The developed method follows a three-step approach of (i) initial pre-processing; (ii) data enhancement/filtering; and (iii) wavelet-based, multi-scale change detection. The stand-alone property of our approach is the high flexibility in applying the change detection approach to a wide range of change detection problems. The performance of the developed approach is demonstrated using synthetic data as well as a real-data application to wildfire progression near Fairbanks, Alaska