A point target detection algorithm for IR image is proposed based on spatial statistics. Firstly, according to the spatial correlation of atmosphere background in infrared image, the spatial statistical analysis method is adopted, and a background suppression algorithm based on Kriging is put forward. Secondly, using peak detection algorithm merged with Kriging, the problem of high false-alarm probability for adaptive threshold filter is solved by dual channel filter. The result shows that the detection probability of the algorithm reaches to 99 percent when the input SCR is no less than 6 and probability of false alarm is between 1×10-3 and 1×10-4.
IR focal plane arrays typically contain bad pixels. Bad pixels have to be corrected because those can significantly impair
the performance of target-detection algorithms. On the other hand, particularly as an aid to visual interpretation, it is
desirable to replace the bad pixels. IR image contains spatial information and is correlative in spatial domain. In spatial
statistics the semivariogram is an important function that relates semivariance to sampling lag. This function can
characterize the spatial dependence of each point on its neighbor and provide a concise and unbiased description of the
scale and pattern of spatial variability. One of the main reasons for deriving semivariogram is to use it in the process of
estimation. Kriging is an interpolation and estimation technique that considers both the distance and the degree of
variation between known data points when estimating values in unknown areas. In this paper a new technique based on
spatial statistics is developed for bad pixel replacement. The main objective of the technique is to replace bad pixels
through Kriging estimation. Theory analysis and experiments show that the method is reasonable and efficient.
Background suppression is an effective method for extracting the signal of target in infrared remote sensing image.
Background clutter contains spatial information and is correlative in spatial domain. In spatial statistics the
semivariogram is an important function that relates semivariance to sampling lag. This function can characterize the
spatial dependence of each point on its neighbor and provide a concise and unbiased description of the scale and pattern
of spatial variability. One of the main reasons for deriving semivariogram is to use it in the process of estimation.
Kriging is an interpolation and estimation technique that considers both the distance and the degree of variation between
known data points when estimating values in unknown areas. A kriged estimate is a weighted linear combination of the
known sample values around the point to be estimated. In this paper a new algorithm based on spatial statistics is
developed for IR background suppression. The main objective of the algorithms is to suppress background clutter
through Kriging estimation. Theory analysis and experiments show that the method is reasonable and efficient.
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