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This paper describes the use of combination of multiresolution image representation and data compression techniques to reduce transmission time and permit early recognition of images. Data compression removes the inherent redundancies in the image data to reduce the overal data volume.
This report describes a dataset of color, stereo images of ordnance collected at a live-fire test range near Nellis Air Force Base.
In this paper we propose to utilize a new concept of discrete directional Gabor frames for automatic image registration. The directional Gabor representations have been shown to provide more accurate feature extraction than directional wavelet transforms for images where texture is the dominant feature. Initial experimental results are presented here which indicate that discrete directional Gabor frames exhibit strong correlations, which indicates that they are likely to improve the existing image registration toolbox.
Radar image simulation techniques are used to determine the character of SAR images of area-extensive targets, for the cases of flat underlying terrain and of moderate relief, with a view to the severity of elevation change effects on the detection and recognition of boundaries and shapes. The experiment, which demonstrated these effects on shapes and boundaries, was performed in order to establish Seasat-A SAR performance. Attention is given to the geometry/propagation effects in range perspective imaging that must be known for information extraction.
For many applications, accurate and fast image registration of large amounts of multi-source data is the first necessary step before subsequent processing and integration. Image registration is defined by several steps and each step can be approached by various methods which all present diverse advantages and drawbacks depending on the type of data, the type of applications, the a prior information known about the data and the type of accuracy that is required. This paper will first present a general overview of remote sensing image registration and then will go over a few specific methods and their applications
For many applications, accurate and fast image registration of large amounts of multi-source data is the first necessary step before subsequent processing and integration. Image registration is defined by several steps and each step can be approached by various methods which all present diverse advantages and drawbacks depending on the type of data, the type of applications, the a priori information known about the data and the type of accuracy that is required. This paper will first present a general overview of remote sensing image registration and then will go over a few specific methods and their applications.
The LANDSAT Thematic Mapper (TM) scanner records reflected solar energy from the earth's surface in six wavelength regions, or bands, and one band that records emitted energy in the thermal region, giving a total of seven bands. Useful research was extracted about terrain morphometry from remote sensing measurements and this information is used in an image-based terrain model for selected coastal geomorphic features in the Great Salt Lake Desert (GSLD). Technical developments include the incorporation of Aerial Profiling of Terrain System (APTS) data in satellite image analysis, and the production and use of 3-D surface plots of TM reflectance data. Also included in the technical developments is the analysis of the ground control point spatial distribution and its affects on geometric correction, and the terrain mapping procedure; using satellite data in a way that eliminates the need to degrade the data by resampling. The most common approach for terrain mapping with multispectral scanner data includes the techniques of pattern recognition and image classification, as opposed to direct measurement of radiance for identification of terrain features. The research approach in this investigation was based on an understanding of the characteristics of reflected light resulting from the variations in moisture and geometry related to terrain as described by the physical laws of radiative transfer. The image-based terrain model provides quantitative information about the terrain morphometry based on the physical relationship between TM data, the physical character of the GSLD, and the APTS measurements.
Pattern recognition invariant to image rotations of up to 75 deg, using a single filter, has been demonstrated for binary synthetic discriminant function (BSDF) optical filters, suggesting their use in directed graph-arranged data bases which can be rapidly traversed by means of a filter-plane programmable spatial light modulator (SLM). The filter data base is arranged as a tree structure in which the root node filters are invariant to over 60 deg rotation, while the leaves are 5-deg invariant. Results are presented from experiments with BSDFs designed to recognize in-plane-rotated views of a Space Shuttle Orbiter. Using a magnetooptic SLM that is driven at 350 Hz in the filter plane, orientation identifications requiring less than 30 msec have been achieved after sequencing through only 10 BSDFs.
Aerospace sensor systems, discussing adaptive multimode sensors and multispectral sensing, electronic recognition of image components, laser radar and Raman scattering technique
Research results and accomplishments of sixteen tasks in the following areas are described: (1) corn and soybean scene radiation research; (2) soil moisture research; (3) sampling and aggregation research; (4) pattern recognition and image registration research; and (5) computer and data base services.
Scene radiation and atmospheric effects, mathematical pattern recognition and image analysis, information evaluation and utilization, and electromagnetic measurements and signal handling are considered. Research issues in sensors and signals, including radar (SAR) reflectometry, SAR processing speed, registration, including overlay of SAR and optical imagery, entire system radiance calibration, and lack of requirements for both sensors and systems, etc. were discussed.
Research proposals in mathematical pattern recognition and image analysis are presented.
SIR-A imaging was used in geological studies of sedimentary terrains in the Sierra Madre Oriental, northeastern Mexico. Geological features such as regional strike and dip, bedding, folding and faulting were readily detected on the image. The recognition of morphostructural units in the imagery, coupled with field verification, enabled geological mapping of the region at the scale of 1:250 000. Structural profiling lead to the elaboration of a morphostructural map allowing the recognition of an echelon folds and field trends which were used to postulate the ectonic setting of the region.
Plans for two fundamental research programs aimed at improving an understanding of the physics and mathematics of remote sensing are discussed. The first program, Scene Radiation and Atmospheric Research Program, is concerned with developing the capability to determine biophysical attributes of terrestrial scenes through an improved understanding of the relationships between those properties and remotely sensed radiation. The research issues in the second program, Mathematical Pattern Recognition and Image Analysis, are discussed with reference to the following five categories: preprocessing, digital image representation, object scene inference, computational structures, and continuing studies.
A more advanced cloud pattern analysis algorithm was subsequently developed to take the shape and brightness of the various clouds into account in a manner that is more consistent with the human analyst's perception of GOES cloud imagery. The results of that classification scheme were compared with precipitation probabilities observed from ships of opportunity off the U.S. east coast to derive empirical regressions between cloud types and precipitation probability. The cloud morphology was then quantitatively and objectively used to map precipitation probabilities during two winter months during which severe cold air outbreaks were observed over the northwest Atlantic. Precipitation probabilities associated with various cloud types are summarized. Maps of precipitation probability derived from the cloud morphology analysis program for two months and the precipitation probability derived from thirty years of ship observation were observed.
The development status of optical processing techniques under development at NASA-JPL, NASA-Ames, and NASA-Johnson, is evaluated with a view to their potential applications in future NASA planetary exploration missions. It is projected that such optical processing systems can yield major reductions in mass, volume, and power requirements relative to exclusively electronic systems of comparable processing capabilities. Attention is given to high-order neural networks for distortion-invariant classification and pattern recognition, multispectral imaging using an acoustooptic tunable filter, and an optical matrix processor for control problems.
A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.
Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern shaping. The interesting thing about D-C synthesis is that the side lobes have the same amplitude. Five-element arrays were used. Again, 41 pattern samples were used for the input. Nine actual D-C patterns ranging from -10 dB to -30 dB side lobe levels were used to train the network. A comparison between simulated and actual D-C techniques for a pattern with -22 dB side lobe level is shown. The goal for this research was to evaluate the performance of neural network computing with antennas. Future applications will employ the backpropagation training algorithm to drastically reduce the computational complexity involved in performing EM compensation for surface errors in large space reflector antennas.