Download or read book Automatic Target Recognition in Hyperspectral Images Using Wavelet Decomposition and Neural Ldentifier written by 蘇鴻昇 and published by . This book was released on 2001 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Target Recognition Wavelet Transforms and Stereo Matching written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stereo Imaging is very important in the analysis of aerial imagery. The proposed project considers the problem of stereo matching using wavelet transforms. In our method, the wavelet transformation of a signal using a wavelet basis with nth vanishing moment is regarded as nth edge of the signal after some smoothing operation. We then analyze the physical meaning of each channel of the sub-images decomposed by a wavelet. Eased on the analysis, we proposed a hierarchical algorithm that combines both feature-based and pixel-based methods together, to compute the disparity of the binocular stereo image. We have tested the proposed algorithm on a large number of natural and synthetic scenes. Our method is effective on stereo image pairs with large disparities that is a case, which is very important for applications as structure from stereo. We also proposed a general wavelet-based hierarchical matching scheme which involves a dynamic detection of interesting points as feature points at different levels of sub-band images via wavelet transform, and adaptive threshold selection based on compactness measures, a guided searching strategy for the best matching from coarse-to-fine levels. Also, some other problems are investigated including the extraction of 3D lines from range images, and dynamic shape retrieval by curve matching and active contour models, and applications of fuzzy logic to project selection. The project falls under the Nebraska DEPSCOR Research Priority Area of Information and Communications Science and Technology awarded by the Ballistic Missile Development Agency (BMDO) and it is administered through the AFOSR.
Download or read book Hyperspectral Image Analysis written by Saurabh Prasad and published by Springer Nature. This book was released on 2020-04-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Download or read book Automatic Target Recognition written by and published by . This book was released on 2007 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Target Recognition Using Wavelet Based Vector Quantization written by and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: An automatic target recognition classifier is described that uses a set of dedicated vector quantizers (VQs) in the wavelet domain. The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition is used to split this region into several subbands. A dedicated VQ codebook is then generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory characteristics. Finally, a path selector was designed to speed up the recognition process at the expense of a tolerable degradation in the recognition rate.
Download or read book Physics of Automatic Target Recognition written by Firooz Sadjadi and published by Springer Science & Business Media. This book was released on 2007-09-04 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the roles of sensors, physics–based attributes, classification methods, and performance evaluation in automatic target recognition. It details target classification from small mine–like objects to large tactical vehicles. Also explored in the book are invariants of sensor and transmission transformations, which are crucial in the development of low latency and computationally manageable automatic target recognition systems.
Download or read book Automatic Target Recognition XIII written by Firooz A. Sadjadi and published by SPIE-International Society for Optical Engineering. This book was released on 2003 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Target Recognition for Hyperspectral Imagery Using High Order Statistics written by and published by . This book was released on 2006 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective.
Download or read book Automatic Target Recognition Using a Modular Neural Network written by and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modular neural network classifier has been applied to the problem of automatic target recognition (ATR) of targets in forward-looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks operating on features extracted from a local portion of a target image. The classification decisions of the individual networks are combined to determine the final classification. Experiments show that decomposition of the input features results in performance superior to a fully connected network in terms of both network complexity and probability of classification. The classifier's performance is further improved by the use of multiresolution features and by the introduction of a higher level neural network on top of the expert networks, a method known as stacked generalization. In addition to feature decomposition, we implemented a data decomposition classifier network and demonstrated improved performance. Experimental results are reported on a large set of FLIR images.
Download or read book Automatic Target Recognition written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2024-05-04 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is Automatic Target Recognition The capacity of an algorithm or device to recognize targets or other objects based on data acquired from sensors is referred to as automatic target recognition, an abbreviation for these capabilities. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Automatic Target Recognition Chapter 2: Computer Vision Chapter 3: Radar Chapter 4: Doppler Radar Chapter 5: Synthetic-aperture Radar Chapter 6: Imaging Radar Chapter 7: Beamforming Chapter 8: Pulse-Doppler Radar Chapter 9: Passive Radar Chapter 10: Inverse Synthetic-aperture Radar (II) Answering the public top questions about automatic target recognition. (III) Real world examples for the usage of automatic target recognition in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Automatic Target Recognition.
Download or read book A New Approach to Automatic Target Recognition Using Wavelet Transforms written by Anitha Panapakkam and published by . This book was released on 1994 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Image Based Automatic Target Recognition written by and published by . This book was released on 2000 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: An image-based automatic target recognition (IATR) is basically a tactical decision aid that integrates all of the available information and produces a dynamic composite picture of a target for visualization and evaluation by an operator. In modem warfare, it has become an indispensable tool for precision strikes and surveillance missions of defense weapon systems. An IATR processes imagery data received from diverse imaging sensors for the purpose of target detection and recognition in real time. Using Wavelet Transforms, images are fused at different resolution levels to obtain a fused image. It has also been demonstrated that fusing the wavelet coefficient images directly can enhance the recognition contents of the image. This composite image contains all the information of interest for an IATR. In addition, an evaluation methodology based on visual perception and on statistical properties of the fused image is presented.
Download or read book Automatic Target Recognition for Hyperspectral Imagery written by Kelly D. Friesen and published by . This book was released on 2012 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Target Recognition Using Wavelet Transforms and Hidden Markov Model for High Range Resolution Profiles written by Radhika Prakash and published by . This book was released on 2001 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Target Recognition Using High Range Resolution Data written by and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new algorithm is presented for Automatic Target Recognition (ATR) using High Range Resolution (HRR) profiles as opposed to traditional Synthetic Aperture Radar (SAR) images. ATR performance using SAR images degrades considerably in case of moving targets due to blurring caused in the cross-range domain. ATR based on HRR profiles, which are formed without Fourier transform in the cross-range, is expected to have superior performance for moving targets with the proposed method. One of the major contributions of this project so far has been the utilization of Eigen-templates as ATR features that are obtained via Singular Value Decomposition (SVD) of HRR profiles. SVD analysis of a large class of HRR data revealed that the Range-space eigenvectors corresponding to the largest singular value accounted for more than 90% of target energy. Hence, it has been proposed that the Range-space Eigen-vectors be used as templates for classification. The effectiveness of data normalization and Gaussianization of profile data in improving classification performance is also studied. With extensive simulation studies it is shown that the proposed Eigen-template based ATR approach provides consistent superior performance with recognition rate reaching 99.5% for the four class XPATCH database. This research project is being conducted in direct collaboration with the Sensors Directorate's ATR Assessment Branch, Wright Laboratories, Wright-Patt AFB, Dayton, Ohio, where it is being monitored by Dr. Rob Williams. A primary objective df this collaborative effort is to complement and augment various other ongoing research activities being conducted or supported by the Wright Labs ATR research team.
Download or read book Aided Automatic Target Detection Using Reflective Hyperspectral Imagery for Airborne Applications written by and published by . This book was released on 1998 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents an algorithm to support airborne, real-time automatic target detection using combined EO/IR spatial and spectral discriminants for remote sensing surveillance and reconnaissance applications. The algorithm presented in this paper is sufficiently robust and optimized to accommodate high throughput, real-time, sub-pixel, hyperspectral target detection, and can also be used to support man-in-the loop or automatic target detection. The essence of this algorithm is the ability to select the adaptive endmember spectral signatures in real-time, regardless of target, background, and system related effects such as atmospheric conditions, calibration or sensor artifacts. Based on the selected endmembers, the spectral angle of the endmembers is used as the discriminant for target detection or terrain identification. The detection performance and false alarm rate (FAR) including the performances of different combinations of individual bands will be quantified. Statistical analysis including class distributions, various moments of hyperspectral data, and the endmember spectral signatures is examined. The Forest Radiance I database is collected with the HYDICE hyperspectral sensor (reflective spectral band of 0.4um to 2.5um) at Aberdeen U. S. Army Proving Ground in Maryland. The data set covers an area of about 10 sq km.
Download or read book Application of Wavelets to Automatic Target Recognition written by and published by . This book was released on 1995 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Application of Wavelets to Automatic Target Recognition, ' is the second phase of multiphase project to insert compactly supported wavelets into an existing or near-term Department of Defense system such as the Longbow fire control radar for the Apache Attack Helicopter. In this contract, we have concentrated mainly on the classifier function. During the first phase of the program ('Application of Wavelets to Radar Data Processing'), the feasibility of using wavelets to process high range resolution profile (HRRP) amplitude returns from a wide bandwidth radar system was demonstrated. This phase obtained fully polarized wide bandwidth radar HRRP amplitude returns and processed, them with wavelet and wavelet packet or (best basis) transforms. Then, by mathematically defined nonlinear feature selection, we showed that significant improvements in the probability of correct classification are possible, up to 14 percentage points maximum (4 percentage points average) improvement when compared to the current classifier performance. In addition, We addressed the feasibility of using wavelet packets' best basis to address target registration, man made object rejection, clutter discriminations, and sythetic aperture radar scene speckle removal and object registration.
