Speckle appearing in synthetic aperture radar (SAR) images is generated by coherent interference of radar echoes from target scatters. Basically, speckle noise has the nature of a multiplicative noise. In this paper p...
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Speckle appearing in synthetic aperture radar (SAR) images is generated by coherent interference of radar echoes from target scatters. Basically, speckle noise has the nature of a multiplicative noise. In this paper procedures for defining and verifying a statistical noise model are developed, and two multiplicative noise-smoothing algorithms are pre-sented. These two algorithms are computationally efficient and have the potential of achieving real-time or near-real-time processing. Several SEASAT SAR and SIR-B (Shuttle Imaging Radar) images are used for illustration.
Delayed Decision Feedback Sequence Estimation (DDFSE) is a new signal processing algorithm for the detection of signals in the presence of noise'. This paper describes the role of DDFSE in digital recording. The p...
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It is well known that detector response nonuniformity results in pattern noise with staring sensors that is a severe problem in the infrared due to the low intrinsic contrast of IR imagery. The pattern noise can be co...
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It is well known that detector response nonuniformity results in pattern noise with staring sensors that is a severe problem in the infrared due to the low intrinsic contrast of IR imagery. The pattern noise can be corrected by electronic processing; however, the ability to correct for pattern noise is limited by the interaction of interscene and intrascene variability with the dynamic range of the processor (number of bits) and, depending upon the algorithm used, by nonlinearities in the detector response. This paper quantifies these limitations and describes the interaction of detector gain nonuniformity and detector nonlinearities. Probabilistic models are developed to determine the maximum sensitivity that can be obtained using a two-point algorithm to correct a nonlinear response curve over a wide temperature range. Curves that permit a prediction of the noise equivalent differential temperature (NEAT) under varying circumstances are presented. A piecewise linear approach to dealing with severe detector response nonlinearities is presented and analyzed for its effectiveness.
Recent progress in coherent optical pattern recognition is reviewed. Emphasis is given to techniques for multiclass distortion-invariant pattern recognition. Real-time and practical architectures and algorithms are de...
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Recent progress in coherent optical pattern recognition is reviewed. Emphasis is given to techniques for multiclass distortion-invariant pattern recognition. Real-time and practical architectures and algorithms are described, and recent results on extensive data bases are noted. Feature-extraction and correlation architectures are chosen as the two major approaches. The feature-extraction techniques considered include Fourier coefficients, chord distributions, and moments. The correlator approaches emphasize the use of synthetic discriminant functions to achieve multiclass distortion-invariant pattern recognition.
A number of applications require the precise tracking or position estimation of an object unresolved in the system optics. This paper evaluates several one-dimensional interpolation algorithms (odd N-point centroids, ...
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A number of applications require the precise tracking or position estimation of an object unresolved in the system optics. This paper evaluates several one-dimensional interpolation algorithms (odd N-point centroids, N = 3, 5, 7, 9, and three-point and five-point quadratic curve fits) designed to make these estimates to subpixel accuracy. Analytic, Monte Carlo, and experimental results are presented. The tracking sensor examined was a scanning linear array of infrared detectors assumed to be background-limited. The detector size and physical spacing were varied parametrically, with realistic fabrication constraints, to determine the relative performance and to obtain the optimum configuration. The optics blur spot was assumed Gaussian. The sources of error considered to affect the algorithm performance were the systematic algorithm bias, the random noise, and the postcalibration residual detector responsivity nonuniformities. Track accuracy improves with signal-to-noise ratio (SNR), until limited by algorithm inaccuracies or focal-plane nonuniformity. Among the algorithms tested, the three-point centroid performs best, provided that systematic algorithm bias is corrected. An experimental infrared tracking focal plane, used in a tracker simulation, closely confirmed the analysis. With the three-point algorithms, an experimental accuracy to smaller than 1/100 a detector (<1/250 a blur spot) was obtained at high signal-to-noise ratios.
A computational model of the deterministic and stochastic processes involved in remote sensing is used to study and compare the performance of sensor spectral responses and data processing algorithms for classifying s...
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A computational model of the deterministic and stochastic processes involved in remote sensing is used to study and compare the performance of sensor spectral responses and data processing algorithms for classifying spectral features. The simulated spectral responses include those of the U.S. Landsat Thematic Mapper (TM) and the French Systeme Probatoire d'Observation de la Terre (SPOT). The simulated data processing algorithms include the computationally simple boundary approximation method (BAM) to discriminate between general target categories such as vegetation, bare land, water, snow, and clouds, and the maximum likelihood (MLH) and mean-square distance (MSD) classifications to discriminate between specific targets such as various crop types.
Synthetic discriminant functions (SDFs) allow distortion invariance to be achieved in optical correlators, thus making such systems more practical. The synthesis of four different types of SDFs is reviewed. Their perf...
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Synthetic discriminant functions (SDFs) allow distortion invariance to be achieved in optical correlators, thus making such systems more practical. The synthesis of four different types of SDFs is reviewed. Their performance in extensive projection tests on 144 images is presented, together with initial performance tests in the presence of noise. This distortion-invariant and shift-invariant pattern recognition algorithm can also be implemented digitally.
Several useful algorithms in image processing involve the spreading of a certain pixel state over large areas and distances. Very often this is a kind of growth from a "seed" conditioned by a binary ...
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Several useful algorithms in image processing involve the spreading of a certain pixel state over large areas and distances. Very often this is a kind of growth from a "seed" conditioned by a binary mask image so that the final states of labels form connected regions. This labeling operation may well be considered to be the archetype of the propagating operations we have in mind but there are several others, e.g. distance mapping in a plane with obstacles.1,2.
The ICOS 20000 system is able to process gray-level image data of a high spatial resolution at a high speed. A large number of data reduction and feature extraction algorithms are hardware implemented. This allows the...
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This paper presents some theoretical analysis and applications of adaptive filtering techniques for the detection of dim targets in the presence of highly structured background clutter. The computer algorithms carry o...
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This paper presents some theoretical analysis and applications of adaptive filtering techniques for the detection of dim targets in the presence of highly structured background clutter. The computer algorithms carry out spatial, temporal, and multispectral filtering processes of forward looking infrared (FLIR) images taken at various times and in different spectral bands. These images were obtained from the Air Force TABILS data base. The basic criterion to drive these adaptive processes is based upon the minimum-mean-square error algorithm. The solution to the problem is to find a set of filter coefficients that will achieve the minimum mean square error at the output of the adaptive filter. The resulting Wiener-Hopf equation involves inverting the covariance matrix. Direct inversion of the covariance matrix, however, is a time-consuming process. To relax the computation complexity, a rapidly convergent adaptive algorithm based upon iteratively estimating the inverse of the covariance matrix was developed. The filtering operation consists of updating the inverse matrix in a sample-by-sample manner. This algorithm lends to a simplified hardware structure because it eliminates the matrix inversion process.
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