A Gaussian Process Regression Approach for Fusion of Remote Sensing Images for Oil Spill Segmentation

Synthetic Aperture Radar (SAR) satellite systems are very efficient in oil spill monitoring due to their capability to operate under all weather conditions. This paper presents a framework using Gaussian process (GP) to fuse SAR images of different modalities and to segment dark areas (assumed oil spill) for oil spill detection. A new covariance function; a product of an intrinsically sparse kernel and a Rational Quadratic Kernel (RQK) is used to model the prior of the estimated image allowing information to be transferred. The accuracy performance evaluation demonstrates that the proposed framework has 37% less RMSE per pixel and a compelling enhancement visually when compared with existing methods

Advanced Methods for Fusion of Remote Sensing Images and Oil Spill Segmentation

Remote sensing systems on board satellites (spaceborne) or aircraft (airborne) have continued to play significant role in disaster management and mitigation, including for oil spill detection due to their ability to obtain wide area coverage images and other data from a distance. A single remotely sensed image can cover hundreds of kilometres of the earth surface enabling wider monitoring and change detection observation. When oil spill occur, remote sensing systems equipped with different sensors covering the spectral bands of the electro-magnetic spectrum are deployed to obtain images for damage assessment, scientific analysis or to ascertain spill location, amount of oil spilled and the type of oil for efficient planning, management and illegal dumping of ballasts identification for legal actions. In the design of such remote sensing systems, there are usually considerate trade-offs that are inevitable due technological limitations of such systems, resulting in spatial and spectral amendments. Panchromatic sensors for example obtain images at high spatial resolutions but lower spectral resolutions, while hyperspectral images obtain high spectral images but in lower spatial resolutions. Additionally, optical systems depend on external energy sources to obtain the images while others can acquire data irrespective of weather conditions. By combining data originating from different sources, scientists, analyst and planners can have images of higher quality than the individual images and can take advantage of the complimentary information embedded in diverse data acquired. This thesis presents a new framework for oil spill detection by combining data originating from different imaging sensors of remote sensing systems. Firstly, the new framework for oil spill segmentation utilises the fusion of images to improve image quality and to take ad-vantage of complimentary information available in the different resolutions of SAR images. The framework adopts the wavelet image fusion technique where the individual images are converted from spatial to frequency domain and decomposed to approximations and de-tail coefficients, allowing image properties to be transferred using a maximum fusion rule. While this method improves spatial resolution of images and retains colour information, it is observed that the scale of decomposition needs to be sensibly selected since smaller scales creates mosaic effects and large scale values causes loss of colour contents making it unsuitable for images with different spectral channels. To solve the problem of multi-modality in images, a Gaussian Process (GP) regression approach is utilised using a custom covariance that learns the geometry and intensity of pixels and also handles the change of support problem inherent in multi-resolution images. Established performance metrics in the literature are used to evaluate the quality of the fused images when compared with a reference data. Additionally, a qualitative and quantitative review of pansharpening methods for hyper-spectral images is carried out specifically for the oil spill detection application. The pansharpened results are compared in terms of un-mixing performance with a reference hyper-spectral image. This re-view can help researchers interested in this field of study to determine what methods are best for pansharpening and un-mixing and to answer the question of whether pansharpening improves un-mixing result. This can be extended for other applications that include weather forecasting, spectral analysis etc. Lastly, the a new covariance kernel is developed to solve image fusion problems in multi-band images by treating differently each spatial and spectral channels as input to the Gaussian process allowing different spatial and spectral pixels of the images to be learned and combined. The developed approach allows the transfer of information between different image modalities enabling local structure of high spatial resolution images that forms the base of the estimated image to be recovered. The developed fusion approaches achieves compelling enhancement when compared with the state of the art. Furthermore, segmentation is done on the fused and reference images with the developed fused image picking up more objects from the image than other methods. This can be attributed to the ability of the approach to sharpen the resolution of the spectral channels that supports pixel coordinates from high spatial image that improves edges of the image

Oil Spill Segmentation in Fused Synthetic Aperture Radar Images

Synthetic Aperture Radar (SAR) satellite systems are very efficient in oil spill monitoring due to their capability to operate under all weather conditions. Systems such as the Envisat and RADARSAT have been used independently in many studies to detect oil spill. This paper presents an automatic feature based image registration and fusion algorithm for oil spill monitoring using SAR images. A range of metrics are used to evaluate the performance of the algorithm and to demonstrate the benefits of fusing SAR images of different modalities. The proposed framework has shown 45% improvement of the oil spill location when compared with the individual images before the fusio

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

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

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Automatic Delineation of Water Bodies in SAR Images with a Novel Stochastic Distance Approach

Coastal regions and surface waters are among the fundamental biological and social development resources worldwide. For this reason, it is essential to thoroughly monitor these regions to determine and characterize their geographical features and environmental health. These geographical regions, however, present several monitoring challenges when using remotely sensed imagery. Small water bodies tend to be surrounded by swamps, marshes, or vegetation, making accurate border detection difficult. Coastal waters, in turn, experience several phenomena due to winds, undercurrents, and waves, which also hamper the detection of environmental hazards like oil spills. In this work, we propose an automated segmentation algorithm that can be applied to these targets in airborne and spaceborne SAR images. The method is based on pointwise detection in fuzzy borders using a parameter estimation of the (Formula presented.) distribution, which has been successfully used in similar contexts. The underlying assumption is that the sought-for border separates regions with different textures, each having different distribution parameters. Then, stochastic distances can identify the most likely point where this parameter change occurs. A curve interpolation algorithm then estimates the actual contour of the body given the detected points. We assess the adequacy of eight stochastic distances that are mostly applied in the literature. We evaluate the performance of our method in terms of similarity between true and detected boundaries on simulated and actual SAR images, achieving promising results. The performance of our proposal is assessed by Hausdorff distance and Intersection over Union. In the case of synthetic data, the selection of the best stochastic distance depends on the parameters of the (Formula presented.) distribution. In contrast, the harmonic-mean and triangular distances produced the best results in detecting borders in three actual SAR images of lagoons. Finally, we present the results of our proposal applied to an image with oil spills using Bhattacharyya, Hellinger, and Jensen–Shannon distances.Fil: Rey, Andrea Alejandra. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Revollo Sarmiento, Natalia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Frery, Alejandro César. Victoria University Of Wellington; Nueva ZelandaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version