Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Image Processing for Ice Parameter Identification in Ice Management

Various types of remotely sensed data and imaging technology will aid the development of sea-ice observation to, for instance, support estimation of ice forces critical to Dynamic Positioning (DP) operations in Arctic waters. The use of cameras as sensors for offshore operations in ice-covered regions will be explored for measurements of ice statistics and ice properties, as part of a sea-ice monitoring system. This thesis focuses on the algorithms for image processing supporting an ice management system to provide useful ice information to dynamic ice estimators and for decision support. The ice information includes ice concentration, ice types, ice floe position and floe size distribution, and other important factors in the analysis of ice-structure interaction in an ice field. The Otsu thresholding and k-means clustering methods are employed to identify the ice from the water and to calculate ice concentration. Both methods are effective for model-ice images. However, the k-means method is more effective than the Otsu method for the sea-ice images with a large amounts of brash ice and slush. The derivative edge detection and morphology edge detection methods are used to try to find the boundaries of the ice floes. Because of the inability of both methods to separate connected ice floes in the images, the watershed transform and the gradient vector flow (GVF) snake algorithm are applied. In the watershed-based method, the grayscale sea-ice image is first converted into a binary image and the watershed algorithm is carried out to segment the image. A chain code is then used to check the concavities of floe boundaries. The segmented neighboring regions that have no concave corners between them are merged, and over-segmentation lines are removed automatically. This method is applicable to separate the seemingly connected floes whose junctions are invisible or lost in the images. In the GVF snake-based method, the seeds for each ice floe are first obtained by calculating the distance transform of the binarized image. Based on these seeds, the snake contours with proper locations and radii are initialized, and the GVF snakes are then evolved automatically to detect floe boundaries and separate the connected floes. Because some holes and smaller ice pieces may be contained inside larger floes, all the segmented ice floes are arranged in order of increasing size after segmentation. The morphological cleaning is then performed to the arranged ice floes in sequence to enhance their shapes, resulting in individual ice floes identification. This method is applicable to identify non-ridged ice floes, especially in the marginal ice zone and managed ice resulting from offshore operations in sea-ice. For ice engineering, both model-scale and full-scale ice will be discussed. In the model-scale, the ice floes in the model-ice images are modeled as square shapes with predefined side lengths. To adopt the GVF snake-based method for model-ice images, three criteria are proposed to check whether it is necessary to reinitialize the contours and segment a second time based on the size and shape of model-ice floe. In the full-scale, sea-ice images are shown to be more difficult than the model-ice images analyzed. In addition to non-uniform illumination, shadows and impurities, which are common issues in both sea-ice and model-ice image processing, various types of ice (e.g., slush, brash, etc.), irregular floe sizes and shapes, and geometric distortion are challenges in seaice image processing. For sea-ice image processing, the “light ice” and “dark ice” are first obtained by using the Otsu thresholding and k-means clustering methods. Then, the “light ice” and “dark ice” are segmented and enhanced by using the GVF snake-based method. Based on the identification result, different types of sea-ice are distinguished, and the image is divided into four layers: ice floes, brash pieces, slush, and water. This then makes it possible to present a color map of the ice floes and brash pieces based on sizes. It also makes it possible to present the corresponding ice floe size distribution histogram

Earth resources: A continuing bibliography with indexes (issue 62)

This bibliography lists 544 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

A procedure for the computation of sea surface advection velocities from satellite thermal band imagery, with applications to the South East Atlantic Ocean

The research was carried out with a view to developing a procedure for the computation of sea surface advection velocities from pairs of NOAA AVHRR infrared images. The procedure was designed for application to the oceanic regions around South Africa and cognisance had to be taken of restrictions imposed by the specific oceanographic conditions, availability of satellite data, as well as the capabilities of the image processing system used. As a first step, a set of image navigation algorithms was developed, based on elliptical orbit and ellipsoidal earth models. Orbit parameters were obtained from TBUS-bulletins and one or more ground reference points had to be identified on each. The navigation algorithms were then used to develop a procedure for the geometric transformation of images to a Mercator map projection. The transformation procedure was evaluated through use of test-images and the results indicated that the maximum errors which could be expected in the computation of advection vectors were 4-5 cm/sin the north/south velocity component and 6-7 cm/sin the east/west component if two images, 12 hours apart in time, were used for the vector computation. An automatic feature tracking method was tested as a means for computing advection velocities but was found to be unsatisfactory. As a result, a 'semi-automated' procedure was developed. This process is essentially a manual (point-wise) feature tracking procedure into which the template matching technique which formed the basis of automated procedures, was incorporated as a labour saving device. Tests indicated a time saving of 20-40 % on the manual procedure and more rapid computation than with the automated procedure. The feature tracking procedure was applied to three sets of AVHRR images of the South East Atlantic. To assess the precision of the vector computation procedure, two independent vector sets were computed. A comparison of the two sets indicated that the rootmean- square deviation in vector magnitude (speed) was about 6-8 cm/sand in the vector direction, about 31° (12° if very small vectors ≤ 6 cm/s are excluded). The computed vectors compared very well with reported results from conventional methods. The derived vector fields also provide the first really detailed description of surface currents in the sea off South Africa: eg. on the flow field in the southern Benguela Current, the circulation associated with Agulhas Current rings, and advective influences on the transport of fish eggs and larvae from the spawning grounds on the Agulhas Bank to the favoured recruitment area off the West Coast

Sonar image interpretation for sub-sea operations

Mine Counter-Measure (MCM) missions are conducted to neutralise underwater explosives. Automatic Target Recognition (ATR) assists operators by increasing the speed and accuracy of data review. ATR embedded on vehicles enables adaptive missions which increase the speed of data acquisition. This thesis addresses three challenges; the speed of data processing, robustness of ATR to environmental conditions and the large quantities of data required to train an algorithm. The main contribution of this thesis is a novel ATR algorithm. The algorithm uses features derived from the projection of 3D boxes to produce a set of 2D templates. The template responses are independent of grazing angle, range and target orientation. Integer skewed integral images, are derived to accelerate the calculation of the template responses. The algorithm is compared to the Haar cascade algorithm. For a single model of sonar and cylindrical targets the algorithm reduces the Probability of False Alarm (PFA) by 80% at a Probability of Detection (PD) of 85%. The algorithm is trained on target data from another model of sonar. The PD is only 6% lower even though no representative target data was used for training. The second major contribution is an adaptive ATR algorithm that uses local sea-floor characteristics to address the problem of ATR robustness with respect to the local environment. A dual-tree wavelet decomposition of the sea-floor and an Markov Random Field (MRF) based graph-cut algorithm is used to segment the terrain. A Neural Network (NN) is then trained to filter ATR results based on the local sea-floor context. It is shown, for the Haar Cascade algorithm, that the PFA can be reduced by 70% at a PD of 85%. Speed of data processing is addressed using novel pre-processing techniques. The standard three class MRF, for sonar image segmentation, is formulated using graph-cuts. Consequently, a 1.2 million pixel image is segmented in 1.2 seconds. Additionally, local estimation of class models is introduced to remove range dependent segmentation quality. Finally, an A* graph search is developed to remove the surface return, a line of saturated pixels often detected as false alarms by ATR. The A* search identifies the surface return in 199 of 220 images tested with a runtime of 2.1 seconds. The algorithm is robust to the presence of ripples and rocks

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems

Vision-based automatic landing of a rotary UAV

A hybrid-like (continuous and discrete-event) approach to controlling a small multi-rotor unmanned aerial system (UAS) while landing on a moving platform is described. The landing scheme is based on positioning visual markers on a landing platform in a detectable pattern. After the onboard camera detects the object pattern, the inner control algorithm sends visual-based servo-commands to align the multi-rotor with the targets. This method is less computationally complex as it uses color-based object detection applied to a geometric pattern instead of feature tracking algorithms, and has the advantage of not requiring the distance to the objects to be calculated. The continuous approach accounts for the UAV and the platform rolling/pitching/yawing, which is essential for a real-time landing on a moving target such as a ship. A discrete-event supervisor working in parallel with the inner controller is designed to assist the automatic landing of a multi-rotor UAV on a moving target. This supervisory control strategy allows the pilot and crew to make time-critical decisions when exceptions, such as losing targets from the field of view, occur. The developed supervisor improves the low-level vision-based auto-landing system and high-level human-machine interface. The proposed hybrid-like approach was tested in simulation using a quadcopter model in Virtual Robotics Experimentation Platform (V-REP) working in parallel with Robot Operating System (ROS). Finally, this method was validated in a series of real-time experiments with indoor and outdoor quadcopters landing on both static and moving platforms. The developed prototype system has demonstrated the capability of landing within 25 cm of the desired point of touchdown. This auto-landing system is small (100 x 100 mm), light-weight (100 g), and consumes little power (under 2 W)