Civilian Target Recognition using Hierarchical Fusion

The growth of computer vision technology has been marked by attempts to imitate human behavior to impart robustness and confidence to the decision making process of automated systems. Examples of disciplines in computer vision that have been targets of such efforts are Automatic Target Recognition (ATR) and fusion. ATR is the process of aided or unaided target detection and recognition using data from different sensors. Usually, it is synonymous with its military application of recognizing battlefield targets using imaging sensors. Fusion is the process of integrating information from different sources at the data or decision levels so as to provide a single robust decision as opposed to multiple individual results. This thesis combines these two research areas to provide improved classification accuracy in recognizing civilian targets. The results obtained reaffirm that fusion techniques tend to improve the recognition rates of ATR systems. Previous work in ATR has mainly dealt with military targets and single level of data fusion. Expensive sensors and time-consuming algorithms are generally used to improve system performance. In this thesis, civilian target recognition, which is considered to be harder than military target recognition, is performed. Inexpensive sensors are used to keep the system cost low. In order to compensate for the reduced system ability, fusion is performed at two different levels of the ATR system { event level and sensor level. Only preliminary image processing and pattern recognition techniques have been used so as to maintain low operation times. High classification rates are obtained using data fusion techniques alone. Another contribution of this thesis is the provision of a single framework to perform all operations from target data acquisition to the final decision making. The Sensor Fusion Testbed (SFTB) designed by Northrop Grumman Systems has been used by the Night Vision & Electronic Sensors Directorate to obtain images of seven different types of civilian targets. Image segmentation is performed using background subtraction. The seven invariant moments are extracted from the segmented image and basic classification is performed using k Nearest Neighbor method. Cross-validation is used to provide a better idea of the classification ability of the system. Temporal fusion at the event level is performed using majority voting and sensor level fusion is done using Behavior-Knowledge Space method. Two separate databases were used. The first database uses seven targets (2 cars, 2 SUVs, 2 trucks and 1 stake body light truck). Individual frame, temporal fusion and BKS fusion results are around 65%, 70% and 77% respectively. The second database has three targets (cars, SUVs and trucks) formed by combining classes from the first database. Higher classification accuracies are observed here. 75%, 90% and 95% recognition rates are obtained at frame, event and sensor levels. It can be seen that, on an average, recognition accuracy improves with increasing levels of fusion. Also, distance-based classification was performed to study the variation of system performance with the distance of the target from the cameras. The results are along expected lines and indicate the efficacy of fusion techniques for the ATR problem. Future work using more complex image processing and pattern recognition routines can further improve the classification performance of the system. The SFTB can be equipped with these algorithms and field-tested to check real-time performance

Applications of a U-Net Variant Neural Network: Image Classification for Vegetation Component Identification in Outdoors Images and Image to Image Translation of Ultrasound Images

Convolutional Neural Networks have been applied in many image applications, for both supervised and unsupervised learning. They have shown their ability to be used in an array of diverse use cases which include but are not limited to image classification, segmentation, and image enhancement tasks. We make use of Convolutional Neural Networks\u27 ability to perform well in these situations and propose an architecture for a Convolutional Neural Network based on a network known as U-Net. We then apply our proposed network to two different tasks, a vegetation classification task for images of outdoors environment, and an image to image translation task for ultrasound images. For the vegetation classification task we make use of our previous work of a green vegetation filter that is used to annotate our data set and then use images that are converted to gray scale to pair with the annotations from the green vegetation filter in order to train our proposed network to classify where generic vegetation appears in an image. For the ultrasound image to image translation task, we show that our proposed network can be used as part of a system which is composed of a set of neural networks, called CycleGAN, that is used to translate ultrasound images from images acquired by a low frequency transducer to an image domain of ultrasound images acquired by a high frequency transducer. We propose using an approach that trains our proposed network to learn local estimations of the two image domains and detail a filtering process that when applied to an ultrasound image, acquired from a low frequency transducer, gives the low frequency transducer ultrasound image the appearance that it was acquired from a high frequency transducer

Use of remote sensing and image processing for identification of wild orchids

A novel multi-technique approach has been applied for the identification and mapping of wild orchids using a combination of remote sensing and spectral image analysis. The five orchid species identified were the common spotted-orchid (Dactylorhiza fuchsia), heath spotted-orchid (Dactylorhiza maculata), pyramidal orchid (Anacamptis pyramidalis), heath fragrant-orchid (Gymnadenia borealis), and the dark-red helleborine (Epipactis atrorubens). Field studies have been done using a hand-held spectrometer operating in the 400–700 nm visible spectrum, photogrammetry using a digital camera as well as a multispectral image camera operating at the specific spectral bands of 450 nm (blue), 560 nm (green), 650 nm (red), 730 nm (red edge) and 840 nm (near-infrared) attached to an unmanned aerial vehicle Data analysis, using the hand-held spectrometer, followed by pattern recognition using principal component analysis and partial least squares-discriminant analysis, have identified the key distinguishing wavelengths for identification of the 5 orchid types as 400, 410, 420 and 560 nm. The use of remote sensing, using the UAV-MSI, and application of a dedicated spectral index has enabled field identification of the orchids. Finally, object-based image analysis of field gathered photogrammetry imagery, has enabled use of shape, size, and color to identify and distinguish orchid species. The developed data analytic tool, using random forest classification, can be used to identify and characterize wild orchids across multiple sites within their short lifespan with an accuracy of 86%. Any longer-term study would provide invaluable information on the diversity and complexity of orchid habitat, population variation both intra- and inter-site location, as well as the impact of climate change

Study of Side Ditch Liners for Highway Application

Over the past few years, the INDOT new materials department has received numerous erosion control products (mostly geosynthetics) to evaluate as alternatives to riprap and concrete in ditch liners. Potential benefits include lower construction costs and better aesthetics over current products. Unfortunately, no specification, design methodology, or classification system currently exists for these erosion control blankets. In this project\u27s phase I, existing information and knowledge on erosion control materials used to line highway side drainage ditches were investigated. From the available technical1iterature (journal and conference publications, other DOTs specifications, manufacturer documentation, independent test laboratory test data), design methodologies, classification system, product approval procedures, and installation methods were reviewed for temporary and permanent geosynthetic erosion control materials. Based on the synthesis of these reviews a design methodology was proposed including design aids (tables, flow charts, and graphs) necessary to perform flexible liner computations. A classification system based on product performance was also proposed. In addition, current design procedures for hard armor materials (fabric formed revetments, concrete block systems, gabions, and riprap) were reviewed. A tentative specification for both flexible and hard armor ditch liners was drafted

Perception of Climate Change in a Pacific Island City

According to the International Panel on Climate Change (IPCC 2007) small island states (SIS) will be severely affected by global climate change. Especially a rising sea level, increased frequency and intensity of extreme weather events and rising temperature will have serious impact on life on small islands in tropical regions. SIS hardly contribute to the emission of greenhouse gases, therefore their main challenge will be focussing on adaptation to prevent further damages

Automatic Extraction of Number of Lanes from Aerial Images for Transportation Applications

Number of lanes is a basic roadway attribute that is widely used in many transportation applications. Traditionally, number of lanes is collected and updated through field surveys, which is expensive especially for large coverage areas with a high volume of road segments. One alternative is through manual data extraction from high-resolution aerial images. However, this is feasible only for smaller areas. For large areas that may involve tens of thousands of aerial images and millions of road segments, an automatic extraction is a more feasible approach. This dissertation aims to improve the existing process of extracting number of lanes from aerial images automatically by making improvements in three specific areas: (1) performance of lane model, (2) automatic acquisition of external knowledge, and (3) automatic lane location identification and reliability estimation. In this dissertation, a framework was developed to automatically recognize and extract number of lanes from geo-rectified aerial images. In order to address the external knowledge acquisition problem in this framework, a mapping technique was developed to automatically estimate the approximate pixel locations of road segments and the travel direction of the target roads in aerial images. A lane model was developed based on the typical appearance features of travel lanes in color aerial images. It provides more resistance to “noise” such as presence of vehicle occlusions and sidewalks. Multi-class classification test results based on the K-nearest neighbor, logistic regression, and Support Vector Machine (SVM) classification algorithms showed that the new model provides a high level of prediction accuracy. Two optimization algorithms based on fixed and flexible lane widths, respectively, were then developed to extract number of lanes from the lane model output. The flexible lane-width approach was recommended because it solved the problems of error-tolerant pixel mapping and reliability estimation. The approach was tested using a lane model with two SVM classifiers, i.e., the Polynomial kernel and the Radial Basis Function (RBF) kernel. The results showed that the framework yielded good performance in a general test scenario with mixed types of road segments and another test scenario with heavy plant occlusions

Eucalyptus globulus Labill. regeneration from seeds in Portugal´s mainland

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia /ULEucalyptus globulus Labill. is a plant species native to SE Australia, Tasmania and adjacent islands. It was introduced in Portugal in the mid-19th century. In 2005/06, it was the most abundant tree species in 23% of the afforested area in Portugal’s mainland. Forests dominated by this species are one of the most fire-prone forest types in Portugal. This thesis was aimed to contribute to a better understanding of the naturalization process in Portugal’s mainland, with a special focus on the role of fire in this process. A multiscale approach was used to address the problem. Natural regeneration of E. globulus from seeds occurs in every natural region of the territory and may reach high densities in some locations. Spatial distribution of both wildling occurrence and density is not uniform on national (mainland), regional, local, and stand scales. The existence of seed sources (reproductive trees) is of primary importance. Climatic and soil conditions affect the broad scale distribution of this regeneration and its performance. Site quality and forest management are fundamental on a local scale. Fire clearly plays a relevant role, inducing seed release from burnt trees, providing safe microsites for plant recruitment and development, and allowing for the establishment of plants in the mid-term. Moreover, litter charring enables the early development of E. globulus in otherwise toxic conditions. Maximum wildling densities observed were 0.3 plants m-2 and 9.9 plants m-2 inside unburnt and burnt plantations, respectively. Portuguese plantations, from a region with nationally moderate levels of seminal regeneration, had mean wildling densities 3.1 times higher than Australian plantations, from seven regions either inside or outside the native range. In summary, cultivated trees are able to produce offspring, which grows, establishes and may produce seeds next to parent trees, in many parts of Portugal’s mainland. Therefore, naturalization is in progress and widespread in this territory, and fire does facilitate itN/

Semantic multimedia analysis using knowledge and context

PhDThe difficulty of semantic multimedia analysis can be attributed to the extended diversity in form and appearance exhibited by the majority of semantic concepts and the difficulty to express them using a finite number of patterns. In meeting this challenge there has been a scientific debate on whether the problem should be addressed from the perspective of using overwhelming amounts of training data to capture all possible instantiations of a concept, or from the perspective of using explicit knowledge about the concepts’ relations to infer their presence. In this thesis we address three problems of pattern recognition and propose solutions that combine the knowledge extracted implicitly from training data with the knowledge provided explicitly in structured form. First, we propose a BNs modeling approach that defines a conceptual space where both domain related evi- dence and evidence derived from content analysis can be jointly considered to support or disprove a hypothesis. The use of this space leads to sig- nificant gains in performance compared to analysis methods that can not handle combined knowledge. Then, we present an unsupervised method that exploits the collective nature of social media to automatically obtain large amounts of annotated image regions. By proving that the quality of the obtained samples can be almost as good as manually annotated images when working with large datasets, we significantly contribute towards scal- able object detection. Finally, we introduce a method that treats images, visual features and tags as the three observable variables of an aspect model and extracts a set of latent topics that incorporates the semantics of both visual and tag information space. By showing that the cross-modal depen- dencies of tagged images can be exploited to increase the semantic capacity of the resulting space, we advocate the use of all existing information facets in the semantic analysis of social media