The Effects of Signal and Image Compression of SAR Data on Change Detection Algorithms

With massive amounts of SAR imagery and data being collected, the need for effective compression techniques is growing. One of the most popular applications for remote sensing is change detection, which compares two geo-registered images for changes in the scene. While lossless compression is needed for signal compression, the same is not often required for image compression. In almost every case the compression ratios are much higher in lossy compression making them more appealing when bandwidth and storage becomes an issue. This research analyzes different types of compression techniques that are adapted for SAR imagery, and tests these techniques with three different change detection algorithms. Many algorithms exist that allow large compression ratios, however, the usefulness of the data is always the final concern. It is necessary to identify compression methods that will not degrade the performance of change detection analysis

Super Resolution of Wavelet-Encoded Images and Videos

In this dissertation, we address the multiframe super resolution reconstruction problem for wavelet-encoded images and videos. The goal of multiframe super resolution is to obtain one or more high resolution images by fusing a sequence of degraded or aliased low resolution images of the same scene. Since the low resolution images may be unaligned, a registration step is required before super resolution reconstruction. Therefore, we first explore in-band (i.e. in the wavelet-domain) image registration; then, investigate super resolution. Our motivation for analyzing the image registration and super resolution problems in the wavelet domain is the growing trend in wavelet-encoded imaging, and wavelet-encoding for image/video compression. Due to drawbacks of widely used discrete cosine transform in image and video compression, a considerable amount of literature is devoted to wavelet-based methods. However, since wavelets are shift-variant, existing methods cannot utilize wavelet subbands efficiently. In order to overcome this drawback, we establish and explore the direct relationship between the subbands under a translational shift, for image registration and super resolution. We then employ our devised in-band methodology, in a motion compensated video compression framework, to demonstrate the effective usage of wavelet subbands. Super resolution can also be used as a post-processing step in video compression in order to decrease the size of the video files to be compressed, with downsampling added as a pre-processing step. Therefore, we present a video compression scheme that utilizes super resolution to reconstruct the high frequency information lost during downsampling. In addition, super resolution is a crucial post-processing step for satellite imagery, due to the fact that it is hard to update imaging devices after a satellite is launched. Thus, we also demonstrate the usage of our devised methods in enhancing resolution of pansharpened multispectral images

Progressively communicating rich telemetry from autonomous underwater vehicles via relays

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2012As analysis of imagery and environmental data plays a greater role in mission construction and execution, there is an increasing need for autonomous marine vehicles to transmit this data to the surface. Without access to the data acquired by a vehicle, surface operators cannot fully understand the state of the mission. Communicating imagery and high-resolution sensor readings to surface observers remains a significant challenge – as a result, current telemetry from free-roaming autonomous marine vehicles remains limited to ‘heartbeat’ status messages, with minimal scientific data available until after recovery. Increasing the challenge, longdistance communication may require relaying data across multiple acoustic hops between vehicles, yet fixed infrastructure is not always appropriate or possible. In this thesis I present an analysis of the unique considerations facing telemetry systems for free-roaming Autonomous Underwater Vehicles (AUVs) used in exploration. These considerations include high-cost vehicle nodes with persistent storage and significant computation capabilities, combined with human surface operators monitoring each node. I then propose mechanisms for interactive, progressive communication of data across multiple acoustic hops. These mechanisms include wavelet-based embedded coding methods, and a novel image compression scheme based on texture classification and synthesis. The specific characteristics of underwater communication channels, including high latency, intermittent communication, the lack of instantaneous end-to-end connectivity, and a broadcast medium, inform these proposals. Human feedback is incorporated by allowing operators to identify segments of data thatwarrant higher quality refinement, ensuring efficient use of limited throughput. I then analyze the performance of these mechanisms relative to current practices. Finally, I present CAPTURE, a telemetry architecture that builds on this analysis. CAPTURE draws on advances in compression and delay tolerant networking to enable progressive transmission of scientific data, including imagery, across multiple acoustic hops. In concert with a physical layer, CAPTURE provides an endto- end networking solution for communicating science data from autonomous marine vehicles. Automatically selected imagery, sonar, and time-series sensor data are progressively transmitted across multiple hops to surface operators. Human operators can request arbitrarily high-quality refinement of any resource, up to an error-free reconstruction. The components of this system are then demonstrated through three field trials in diverse environments on SeaBED, OceanServer and Bluefin AUVs, each in different software architectures.Thanks to the National Science Foundation, and the National Oceanic and Atmospheric Administration for their funding of my education and this work

Approximate trigonometric expansions with applications to signal decomposition and coding

Signal representation and data coding for multi-dimensional signals have recently received considerable attention due to their importance to several modern technologies. Many useful contributions have been reported that employ wavelets and transform methods. For signal representation, it is always desired that a signal be represented using minimum number of parameters. The transform efficiency and ease of its implementation are to a large extent mutually incompatible. If a stationary process is not periodic, then the coefficients of its Fourier expansion are not uncorrelated. With the exception of periodic signals the expansion of such a process as a superposition of exponentials, particularly in the study of linear systems, needs no elaboration. In this research, stationary and non-periodic signals are represented using approximate trigonometric expansions. These expansions have a user-defined parameter which can be used for making the transformation a signal decomposition tool. It is shown that fast implementation of these expansions is possible using wavelets. These approximate trigonometric expansions are applied to multidimensional signals in a constrained environment where dominant coefficients of the expansion are retained and insignificant ones are set to zero. The signal is then reconstructed using these limited set of coefficients, thus leading to compression. Sample results for representing multidimensional signals are given to illustrate the efficiency of the proposed method. It is verified that for a given number of coefficients, the proposed technique yields higher signal to noise ratio than conventional techniques employing the discrete cosine transform technique

The 1993 Space and Earth Science Data Compression Workshop

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

Lossy compression and real-time geovisualization for ultra-low bandwidth telemetry from untethered underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2008Oceanographic applications of robotics are as varied as the undersea environment itself. As underwater robotics moves toward the study of dynamic processes with multiple vehicles, there is an increasing need to distill large volumes of data from underwater vehicles and deliver it quickly to human operators. While tethered robots are able to communicate data to surface observers instantly, communicating discoveries is more difficult for untethered vehicles. The ocean imposes severe limitations on wireless communications; light is quickly absorbed by seawater, and tradeoffs between frequency, bitrate and environmental effects result in data rates for acoustic modems that are routinely as low as tens of bits per second. These data rates usually limit telemetry to state and health information, to the exclusion of mission-specific science data. In this thesis, I present a system designed for communicating and presenting science telemetry from untethered underwater vehicles to surface observers. The system's goals are threefold: to aid human operators in understanding oceanographic processes, to enable human operators to play a role in adaptively responding to mission-specific data, and to accelerate mission planning from one vehicle dive to the next. The system uses standard lossy compression techniques to lower required data rates to those supported by commercially available acoustic modems (O(10)-O(100) bits per second). As part of the system, a method for compressing time-series science data based upon the Discrete Wavelet Transform (DWT) is explained, a number of low-bitrate image compression techniques are compared, and a novel user interface for reviewing transmitted telemetry is presented. Each component is motivated by science data from a variety of actual Autonomous Underwater Vehicle (AUV) missions performed in the last year.National Science Foundation Center for Subsurface Sensing and Imaging (CenSSIS ERC

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

Selected topics in video coding and computer vision

Video applications ranging from multimedia communication to computer vision have been extensively studied in the past decades. However, the emergence of new applications continues to raise questions that are only partially answered by existing techniques. This thesis studies three selected topics related to video: intra prediction in block-based video coding, pedestrian detection and tracking in infrared imagery, and multi-view video alignment.;In the state-of-art video coding standard H.264/AVC, intra prediction is defined on the hierarchical quad-tree based block partitioning structure which fails to exploit the geometric constraint of edges. We propose a geometry-adaptive block partitioning structure and a new intra prediction algorithm named geometry-adaptive intra prediction (GAIP). A new texture prediction algorithm named geometry-adaptive intra displacement prediction (GAIDP) is also developed by extending the original intra displacement prediction (IDP) algorithm with the geometry-adaptive block partitions. Simulations on various test sequences demonstrate that intra coding performance of H.264/AVC can be significantly improved by incorporating the proposed geometry adaptive algorithms.;In recent years, due to the decreasing cost of thermal sensors, pedestrian detection and tracking in infrared imagery has become a topic of interest for night vision and all weather surveillance applications. We propose a novel approach for detecting and tracking pedestrians in infrared imagery based on a layered representation of infrared images. Pedestrians are detected from the foreground layer by a Principle Component Analysis (PCA) based scheme using the appearance cue. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm. Simulations with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithms.;Multi-view video alignment is a process to facilitate the fusion of non-synchronized multi-view video sequences for various applications including automatic video based surveillance and video metrology. In this thesis, we propose an accurate multi-view video alignment algorithm that iteratively aligns two sequences in space and time. To achieve an accurate sub-frame temporal alignment, we generalize the existing phase-correlation algorithm to 3-D case. We also present a novel method to obtain the ground-truth of the temporal alignment by using supplementary audio signals sampled at a much higher rate. The accuracy of our algorithm is verified by simulations using real-world sequences

Visually Lossless Perceptual Image Coding Based on Natural- Scene Masking Models

Perceptual coding is a subdiscipline of image and video coding that uses models of human visual perception to achieve improved compression efficiency. Nearly, all image and video coders have included some perceptual coding strategies, most notably visual masking. Today, modern coders capitalize on various basic forms of masking such as the fact that distortion is harder to see in very dark and very bright regions, in regions with higher frequency content, and in temporal regions with abrupt changes. However, beyond these obvious forms of masking, there are many other masking phenomena that occur (and co-occur) when viewing natural imagery. In this chapter, we present our latest research in perceptual image coding using natural-scene masking models. We specifically discuss: (1) how to predict local distortion visibility using improved natural-scene masking models and (2) how to apply the models to high efficiency video coding (HEVC). As we will demonstrate, these techniques can offer 10–20% fewer bits than baseline HEVC in the ultra-high-quality regime

The JPEG2000 still image compression standard

The development of standards (emerging and established) by the International Organization for Standardization (ISO), the International Telecommunications Union (ITU), and the International Electrotechnical Commission (IEC) for audio, image, and video, for both transmission and storage, has led to worldwide activity in developing hardware and software systems and products applicable to a number of diverse disciplines [7], [22], [23], [55], [56], [73]. Although the standards implicitly address the basic encoding operations, there is freedom and flexibility in the actual design and development of devices. This is because only the syntax and semantics of the bit stream for decoding are specified by standards, their main objective being the compatibility and interoperability among the systems (hardware/software) manufactured by different companies. There is, thus, much room for innovation and ingenuity. Since the mid 1980s, members from both the ITU and the ISO have been working together to establish a joint international standard for the compression of grayscale and color still images. This effort has been known as JPEG, the Join