Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE.1146Ysciescopu

Development and implementation of image fusion algorithms based on wavelets

Image fusion is a process of blending the complementary as well as the common features of a set of images, to generate a resultant image with superior information content in terms of subjective as well as objective analysis point of view. The objective of this research work is to develop some novel image fusion algorithms and their applications in various fields such as crack detection, multi spectra sensor image fusion, medical image fusion and edge detection of multi-focus images etc. The first part of this research work deals with a novel crack detection technique based on Non-Destructive Testing (NDT) for cracks in walls suppressing the diversity and complexity of wall images. It follows different edge tracking algorithms such as Hyperbolic Tangent (HBT) filtering and canny edge detection algorithm. The second part of this research work deals with a novel edge detection approach for multi-focused images by means of complex wavelets based image fusion. An illumination invariant hyperbolic tangent filter (HBT) is applied followed by an adaptive thresholding to get the real edges. The shift invariance and directionally selective diagonal filtering as well as the ease of implementation of Dual-Tree Complex Wavelet Transform (DT-CWT) ensure robust sub band fusion. It helps in avoiding the ringing artefacts that are more pronounced in Discrete Wavelet Transform (DWT). The fusion using DT-CWT also solves the problem of low contrast and blocking effects. In the third part, an improved DT-CWT based image fusion technique has been developed to compose a resultant image with better perceptual as well as quantitative image quality indices. A bilateral sharpness based weighting scheme has been implemented for the high frequency coefficients taking both gradient and its phase coherence in accoun

Combat Identification with Sequential Observations, Rejection Option, and Out-of-Library Targets

This research extends a mathematical framework to select the optimal sensor ensemble and fusion method across multiple decision thresholds subject to warfighter constraints for a combat identification (CID) system. The formulation includes treatment of exemplars from target classes on which the CID system classifiers are not trained (out-of-library classes) and enables the warfighter to optimize a CID system without explicit enumeration of classifier error costs. A time-series classifier design methodology is developed and applied, yielding a multi-variate Gaussian hidden Markov model (HMM). The extended CID framework is used to compete the HMM-based CID system against a template-based CID system. The framework evaluates competing classifier systems that have multiple fusion methods, varied prior probabilities of targets and non-targets, varied correlation between multiple sensor looks, and varied levels of target pose estimation error. Assessment using the extended framework reveals larger feasible operating regions for the HMM-based classifier across experimental settings. In some cases the HMM-based classifier yields a feasible region that is 25\% of the threshold operating space versus 1\% for the template-based classifier

Adaptive detection and tracking using multimodal information

This thesis describes work on fusing data from multiple sources of information, and focuses on two main areas: adaptive detection and adaptive object tracking in automated vision scenarios. The work on adaptive object detection explores a new paradigm in dynamic parameter selection, by selecting thresholds for object detection to maximise agreement between pairs of sources. Object tracking, a complementary technique to object detection, is also explored in a multi-source context and an efficient framework for robust tracking, termed the Spatiogram Bank tracker, is proposed as a means to overcome the difficulties of traditional histogram tracking. As well as performing theoretical analysis of the proposed methods, specific example applications are given for both the detection and the tracking aspects, using thermal infrared and visible spectrum video data, as well as other multi-modal information sources

The application of Earth Observation for mapping soil saturation and the extent and distribution of artificial drainage on Irish farms

Artificial drainage is required to make wet soils productive for farming. However, drainage may have unintended environmental consequences, for example, through increased nutrient loss to surface waters or increased flood risk. It can also have implications for greenhouse gas emissions. Accurate data on soil drainage properties could help mitigate the impact of these consequences. Unfortunately, few countries maintain detailed inventories of artificially-drained areas because of the costs involved in compiling such data. This is further confounded by often inadequate knowledge of drain location and function at farm level. Increasingly, Earth Observation (EO) data is being used map drained areas and detect buried drains. The current study is the first harmonised effort to map the location and extent of artificially-drained soils in Ireland using a suite of EO data and geocomputational techniques. To map artificially-drained areas, support vector machine (SVM) and random forest (RF) machine learning image classifications were implemented using Landsat 8 multispectral imagery and topographical data. The RF classifier achieved overall accuracy of 91% in a binary segmentation of artifically-drained and poorly-drained classes. Compared with an existing soil drainage map, the RF model indicated that ~44% of soils in the study area could be classed as “drained”. As well as spatial differences, temporal changes in drainage status where detected within a 3 hectare field, where drains installed in 2014 had an effect on grass production. Using the RF model, the area of this field identified as “drained” increased from a low of 25% in 2011 to 68% in 2016. Landsat 8 vegetation indices were also successfully applied to monitoring the recovery of pasture following extreme saturation (flooding). In conjunction with this, additional EO techniques using unmanned aerial systems (UAS) were tested to map overland flow and detect buried drains. A performance assessment of UAS structure-from-motion (SfM) photogrammetry and aerial LiDAR was undertaken for modelling surface runoff (and associated nutrient loss). Overland flow models were created using the SIMWE model in GRASS GIS. Results indicated no statistical difference between models at 1, 2 & 5 m spatial resolution (p< 0.0001). Grass height was identified as an important source of error. Thermal imagery from a UAS was used to identify the locations of artifically drained areas. Using morning and afternoon images to map thermal extrema, significant differences in the rate of heating were identified between drained and undrained locations. Locations of tiled and piped drains were identified with 59 and 64% accuracy within the study area. Together these methods could enable better management of field drainage on farms, identifying drained areas, as well as the need for maintenance or replacement. They can also assess whether treatments have worked as expected or whether the underlying saturation problems continues. Through the methods developed and described herein, better characterisation of drainage status at field level may be achievable

Real-Time, Multiple Pan/Tilt/Zoom Computer Vision Tracking and 3D Positioning System for Unmanned Aerial System Metrology

The study of structural characteristics of Unmanned Aerial Systems (UASs) continues to be an important field of research for developing state of the art nano/micro systems. Development of a metrology system using computer vision (CV) tracking and 3D point extraction would provide an avenue for making these theoretical developments. This work provides a portable, scalable system capable of real-time tracking, zooming, and 3D position estimation of a UAS using multiple cameras. Current state-of-the-art photogrammetry systems use retro-reflective markers or single point lasers to obtain object poses and/or positions over time. Using a CV pan/tilt/zoom (PTZ) system has the potential to circumvent their limitations. The system developed in this paper exploits parallel-processing and the GPU for CV-tracking, using optical flow and known camera motion, in order to capture a moving object using two PTU cameras. The parallel-processing technique developed in this work is versatile, allowing the ability to test other CV methods with a PTZ system using known camera motion. Utilizing known camera poses, the object\u27s 3D position is estimated and focal lengths are estimated for filling the image to a desired amount. This system is tested against truth data obtained using an industrial system

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Internet and Biometric Web Based Business Management Decision Support

Internet and Biometric Web Based Business Management Decision Support MICROBE MOOC material prepared under IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials Prepared by: A. Kaklauskas, A. Banaitis, I. Ubarte Vilnius Gediminas Technical University, Lithuania Project No: 2020-1-LT01-KA203-07810