多偏波SAR画像を用いた自己組織化マップによる自動目標認識法

合成開口レーダ(SAR:Synthetic Aperture Radar)は，航空機・衛星にアンテナを搭載し，地表面を観測する画像レーダセンサとして有用とされている．SAR は，マイクロ波を用いることで昼夜・天候に依存しない観測が可能であり，広域かつ高分解能な画像を生成する．しかし，SAR 画像で用いる波長は数cm 程度であり，光学画像で用いる波長(数百nm)と大きく異なる．これにより，光学画像と比較して，視覚による目標認識が困難となる．そこで，近年，SAR 画像に対する機械学習等を用いた様々な自動目標認識法(ATR:Automatic Target Recognition)が提案されている．一般に自動目標認識では，前処理として目標領域を抽出する目標領域推定法が必要である．従来の目標領域推定法としてPWF(Polarimetric Whitening Filter)が提案されているが，熱雑音のような白色性干渉信号に対しては目領域推定精度が劣化する．また，従来の自動目標認識法として，ニューラルネットワークなどを用いた手法が提案されているが，雑音や方位方向変化に対してロバストではないという問題点を有する．これに対し，我々は既に，SOM(Self-organizing map)を用いた自動目標認識法を提案しており，分類においてU-matrix 基準によるポテンシャル場を評価することで，認識精度の改善を実現した．しかし，同手法は，単一偏波SAR 画像のみを考慮しており，目標の方位方向誤差に対しては，ロバスト性が不十分であった．一方，複数偏波による散乱データ解析により，目標形状情報の抽出を可能とする研究が多数報告されており，これら多偏波SAR 画像を自動目標認識に用いることで，その精度向上が期待されている．本論文では，まず目標領域抽出のために，各偏波画像のPSNR(Peak Signal-to-Noise Ratio)より重み付け偏波合成法を提案する．さらに，従来のSOM を用いた目標認識法における入力ベクトルを多偏波SAR データに拡張する．ここでは，基準SAR 画像を用いた方位方向補正と円偏波基底変換を導入することで，目標の方位方向誤差に対するロバスト性を向上させる．実験では，X バンドレーダの100 分の1 スケールモデルを想定し，5 つの民間航空機模型を多偏波で観測する．まず，実験データより，PSNR による重み付合成SAR 画像により，目標領域を高精度に抽出することを示す．さらに，円偏波基底変換を用いた提案法より，従来の単一偏波や直線偏波基底を用いた手法よりも目標認識確率が向上したことを示す．電気通信大学201

An estimation-theoretic technique for motion-compensated synthetic-aperture array imaging

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Vita.Includes bibliographical references (p. 351-354).Synthetic-Aperture Radar (SAR) is an imaging technique that achieves high azimuth resolution by using coherent processing to exploit the relative motion between an airborne or spaceborne radar antenna and the imaged target field (effectively synthesizing the effect of a larger aperture array). From an estimation-theoretic perspective, this thesis addresses the following limitations of conventional imaging techniques for the spotlight-mode version of SAR: sidelobe imaging artifacts and loss of resolution for stationary SAR scenes containing high-amplitude scatterers, and blurring and object-displacement artifacts in the presence of moving targets. First, this thesis presents a generalized estimation-theoretic SAR imaging framework which exploits the idea of L1-norm regularization. Some results are included which demonstrate the utility of this approach for reducing sidelobes and improving resolution for stationary SAR images. A parameterized L-norm-based moving-target imaging technique is also presented. For the case of a single moving target, this technique is able to compensate for the blurring due to temporally-constant velocity rigid-body motion (even if the target scatterers are closely-spaced). However, the motion-induced object-displacement compensation performance of this technique is significantly affected by velocity estimation errors. This thesis also presents an estimation-theoretic moving-target SAR imaging framework which uses a multi-dimensional matched-filter for computing a set of scatterer-velocity estimates which are used as initial conditions for an L1-norm-based estimation algorithm which assumes that the target scatterers have temporally-constant spatially-independent velocities. Therefore, this framework is able to image a moving target and nearby high-amplitude stationary clutter simultaneously. This framework also shows potential for imaging targets with non-rigid body motion. However, the motion-induced object-displacement compensation performance of this approach is significantly affected by cross-scatterer interference effects.by Cedric Leonard Logan.Sc.D

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

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided

Space and Earth Sciences, Computer Systems, and Scientific Data Analysis Support, Volume 1

This Final Progress Report covers the specific technical activities of Hughes STX Corporation for the last contract triannual period of 1 June through 30 Sep. 1993, in support of assigned task activities at Goddard Space Flight Center (GSFC). It also provides a brief summary of work throughout the contract period of performance on each active task. Technical activity is presented in Volume 1, while financial and level-of-effort data is presented in Volume 2. Technical support was provided to all Division and Laboratories of Goddard's Space Sciences and Earth Sciences Directorates. Types of support include: scientific programming, systems programming, computer management, mission planning, scientific investigation, data analysis, data processing, data base creation and maintenance, instrumentation development, and management services. Mission and instruments supported include: ROSAT, Astro-D, BBXRT, XTE, AXAF, GRO, COBE, WIND, UIT, SMM, STIS, HEIDI, DE, URAP, CRRES, Voyagers, ISEE, San Marco, LAGEOS, TOPEX/Poseidon, Pioneer-Venus, Galileo, Cassini, Nimbus-7/TOMS, Meteor-3/TOMS, FIFE, BOREAS, TRMM, AVHRR, and Landsat. Accomplishments include: development of computing programs for mission science and data analysis, supercomputer applications support, computer network support, computational upgrades for data archival and analysis centers, end-to-end management for mission data flow, scientific modeling and results in the fields of space and Earth physics, planning and design of GSFC VO DAAC and VO IMS, fabrication, assembly, and testing of mission instrumentation, and design of mission operations center

3D Object Recognition Based On Constrained 2D Views

The aim of the present work was to build a novel 3D object recognition system capable of classifying man-made and natural objects based on single 2D views. The approach to this problem has been one motivated by recent theories on biological vision and multiresolution analysis. The project's objectives were the implementation of a system that is able to deal with simple 3D scenes and constitutes an engineering solution to the problem of 3D object recognition, allowing the proposed recognition system to operate in a practically acceptable time frame. The developed system takes further the work on automatic classification of marine phytoplank- (ons, carried out at the Centre for Intelligent Systems, University of Plymouth. The thesis discusses the main theoretical issues that prompted the fundamental system design options. The principles and the implementation of the coarse data channels used in the system are described. A new multiresolution representation of 2D views is presented, which provides the classifier module of the system with coarse-coded descriptions of the scale-space distribution of potentially interesting features. A multiresolution analysis-based mechanism is proposed, which directs the system's attention towards potentially salient features. Unsupervised similarity-based feature grouping is introduced, which is used in coarse data channels to yield feature signatures that are not spatially coherent and provide the classifier module with salient descriptions of object views. A simple texture descriptor is described, which is based on properties of a special wavelet transform. The system has been tested on computer-generated and natural image data sets, in conditions where the inter-object similarity was monitored and quantitatively assessed by human subjects, or the analysed objects were very similar and their discrimination constituted a difficult task even for human experts. The validity of the above described approaches has been proven. The studies conducted with various statistical and artificial neural network-based classifiers have shown that the system is able to perform well in all of the above mentioned situations. These investigations also made possible to take further and generalise a number of important conclusions drawn during previous work carried out in the field of 2D shape (plankton) recognition, regarding the behaviour of multiple coarse data channels-based pattern recognition systems and various classifier architectures. The system possesses the ability of dealing with difficult field-collected images of objects and the techniques employed by its component modules make possible its extension to the domain of complex multiple-object 3D scene recognition. The system is expected to find immediate applicability in the field of marine biota classification

Multispectral Resource Sampler Workshop

The utility of the multispectral resource sampler (MRS) was examined by users in the following disciplines: agriculture, atmospheric studies, engineering, forestry, geology, hydrology/oceanography, land use, and rangelands/soils. Modifications to the sensor design were recommended and the desired types of products and number of scenes required per month were indicated. The history, design, capabilities, and limitations of the MRS are discussed as well as the multilinear spectral array technology which it uses. Designed for small area inventory, the MRS can provide increased temporal, spectral, and spatial resolution, facilitate polarization measurement and atmospheric correction, and test onboard data compression techniques. The advantages of using it along with the thematic mapper are considered