Currently there is no algorithm which can be adapted to all of the imaging conditions. So, it is necessary for us to
find a method to evaluate the existing ATR (automatic target recognition) algorithm. We do some researches on ATR
algorithm performance evaluation based on test methodology. The basic idea of the algorithm performance evaluation is
to establish the relationship model between the image quality characteristics and the algorithm’s performance. In this
paper, the algorithm performance evaluation’s techniques are studied, which include the algorithm performance
assessment framework, the universal test image database’s creating, and the research of the image quality evaluation
model. Firstly, under the guidance of the orthogonal experimental design method, we construct a universal test image
database which includes the simulation image and the outfield flight data. And then this paper propose a method to
establish the relation model between image quality characteristic and ATR algorithm based on SVM classifier. Finally
we use the model to evaluate algorithm’s performance. We conduct experiments on the matching algorithm’s
performance evaluation. The experimental results show that the proposed evaluation framework is efficient and the
evaluation model is well.
Cloud recognition is the base of weather forecast and the recognition of cloud types is challenging because the texture of
the clouds is extremely variable under different atmospheric conditions. In this paper, we propose a novel method for
ground-based cloud classification. Firstly, the interest operator feature (IO) and the sorted spectral histogram (SSH)
feature are generated from Gabor-filtered images and then they are selected by using the principal component analysis
(PCA), which can reduce the feature's dimension. Secondly the new training set is selected using the supervised
clustering technology. Finally we send the two features to the multi-class SVM classifier, and a voting algorithm is used
to determine the category of each cloud. In practice, we find no single feature is best suited for recognizing all these
classes. The result shows that this method has higher classfication accuracy and lower space complexity than the other
Proc. SPIE. 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications
KEYWORDS: Image fusion, Pattern recognition, Remote sensing, Data processing, Information technology, Associative arrays, Artificial intelligence, Probability theory, Evolutionary algorithms, Current controlled current source
Detecting regions of change in multitemporal remote-sensing images of the same scene taken at different times is of
widespread interest in recent years. In this paper, we propose a new change detection method based on a fusion of multisimilarity
measures. This fusion is performed in the framework of the Dempster-Shafer evidence theory, which allows
you to combine evidence from different sources and arrive at a degree of belief (represented by a belief function) that
takes into account all the available evidence. The proposed algorithm is applied to airport change evaluation based on
two popular Gray-textural similarity measures: grayscale difference and grayscale ratio. Experimental results confirm the
effectiveness of the proposed method.
The aim of the present work is to assess the performance of three-dimensional Double Directional Filtering (TDDDF) algorithm for detecting and tracking a weak moving dim target against a complex cluttered background in infrared image sequences. This paper proposes an novel TDDDF to improve the integrated signal-to-clutter ratio (ISCR) and enhance the three-dimensional directional filter's (TDDF) target energy accumulation ability further. Since the TDDDF do well to whitening noise (or quasi whitening noise) but not so sensitive to complex cloudscene background, prior to the filtering, a newly pre-whitening method termed Spatial-Temporal Adaptive Filtering algorithm is used here to suppress clutter background. Extensive experiment results demonstrate the proposed algorithm's ability in detecting weak dim point target against cloud-cluttered background. Finally, performance comparisons of the proposed algorithm and TDDF, on real IR image data, are presented in which the advantages of the proposed TDDDF filters are shown.
Proc. SPIE. 6044, MIPPR 2005: Image Analysis Techniques
KEYWORDS: Digital signal processing, Lithium, Image processing, Computing systems, Field programmable gate arrays, Telecommunications, Signal processing, Time division multiplexing, Data communications, Evolutionary algorithms
This paper presents a type of real-time image-processing system based on multi- processor (TI 320C6400). Modularization is adopted in this system. The system is multi-bus architecture. In each basic module communication is implemented via local bus. Between modules communication is implemented via so-called "Links" and multi-channel buffered serial ports (McBSP). The system is flexible and scalable and can be programmed as pipelined or SIMD or MIMD architecture to meet the variability of the parallel algorithm of image processing.
Radar scene matching technique has been widely found in many application fields such as remote sensing, navigation, terrain-map match, scenery variance analysis and so on. Radar image geometry is quite different from that of optical satellite imagery, whose imaging is a slanting imaging of electromagnetic microwave reflection. The different characters between radar image and optical satellite images are very distinct, such as the layover distortion of ground-truth and speckle noise, which degrades the image to such an extent that the features are very unclear and difficult to be extracted. So the factors such as the hypsography, ground truth, sensor altitude and imaging time should be taken into account for radar image and optical image matching. In this paper, we develop an image match algorithm based on reference map multi-area selection using fuzzy sets. Image matching is generally a procedure that calculates the similarity measurement between sensed image and the corresponding intercepted image in reference map and it searches the maximum position in the correlation map. Our method adopts a converse matching strategy which selects multi-areas in optical reference map using fuzzy sets as model images, then match them on the sensed image respectively by normalized cross correlation matching algorithm and fuse the match results to get the optimum registered position. Multi-areas selection mainly considers two influence factors such as ground-truth texture features and the hypsography (DEM) of imaging region, which will suppress the influence of great variance imaging region. Experiment results show the method is effective in registering performance and reducing the calculation.
We assess the performance of a novel three-dimensional double directional filtering (3DDDF) algorithm for detecting and tracking weak moving dim targets against a complex cluttered background in infrared (IR) image sequences. This proposed method increases the target energy accumulation ability further than the three-dimensional directional filter (3DDF) method. Prior to the filtering, a new prewhitening method termed a three-dimensional spatialtemporal adaptive prediction filter (TDSTAPF) is used to suppress the cluttered background. Extensive experiment results demonstrate the proposed algorithms' ability to detect weak dim point targets against a complex cloud-cluttered background in real IR image sequence and the performance comparisons of the proposed method and 3DDF.
Rough sets theory is a new mathematical tool to deal with vagueness and uncertainty. It is a promising soft computation method of intellective information processing. A method for speckle intelligent filtering of SAR image based on rough sets theory is presented in this paper, This method is proved to be practical and efficient by the experiments.
The scene matching between side-looking real aperture radar (SLAR) image and synthetic aperture radar (SAR) image is influenced by the terrain height variance because radar imaging in the direction of slanting range. The match algorithm investigated on this paper between real aperture radar image and SAR is based on normalize cross correlation match. In this paper, through analysis the geometric model of side-looking real aperture radar imaging, we propose a method of vertical projection adjust the geometric distortion of side-looking real aperture radar image, and discuss the relation between match performance and the height difference in reference region, which is useful for the evaluation of radar image matching reliability. The simulation experiments of real aperture radar imaging at different altitude proves that the matching precision and robust are improved distinctly after the vertical projection adjustment of the real aperture radar image geometric distortion.
A new numerical model to predict the IR radiative temperature of the ground with different mulch is presented. Based on the energy and water balance principle, the mulched and unmulched ground surface boundary conditions for both heat and water flow are described. The temperature of mulched and unmulched soil region and mulch surface are determined to predict the surface radiative temperature of the ground background. The inputs required for the numerical simulations are weather data, soil thermal and hydraulic properties, and mulch data. Numerical experiments are performed to examine the effect of soil type, mulch kind, and weather conditions on the spatial variations in radiative temperature. 24-hours continuous simulations for each combination of different soil type, mulch kind and mulch width show that the soil type, mulch kind and width, and weather conditions can evidently affect the radiative feature of the ground surface.
By studying 2D coupled heat and water flow within the soil layer with a buried object, a theoretical model for IR imaging buried object sites is presented. The model uses the soil surface energy and water balance equations to determine soil surface temperature. The inputs required for the computer simulations are weather data, solid thermal and hydraulic properties, and object data. Numerical experiments are performed to examine the effect of soil type, object species, and weather conditions on the variations in skin temperature, which show that the object species, object size, and burial depth can evidently affect image feature of the soil surface. The present model reasonably described the soil thermal and hydrological environments and thus can be applied successfully to IR imaging buried object sites.