Threshold Selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.

Hurricane eye morphology extraction from SAR images by texture analysis

Tropical hurricanes are among the most devastating hazards on Earth.Knowledge about its intense inner-core structure and dynamics will improve hurricane forecasts and advisories.The precise morphological parameters extracted from high-resolution spaceborne Synthetic Aperture Radar(SAR)images,can play an essential role in further exploring and monitoring hurricane dynamics,especially when hurricanes undergo amplification,shearing,eyewall replacements and so forth.Moreover,these parameters can help to build guidelines for wind calibration of the more abundant,but lower resolution scatterometer wind data,thus better linking scatterometer wind fields to hurricane categories.In this paper,we develop a new method for automatically extracting the hurricane eyes from C-band SAR data by constructing Gray Level-Gradient Co-occurrence Matrices(GLGCMs).The hurricane eyewall is determined with a two-dimensional vector,generated by maximizing the class entropy of the hurricane eye region in GLGCM.The results indicate that when the hurricane is weak,or the eyewall is not closed,the hurricane eye extracted with this automatic method still agrees with what is observed visually,and it preserves the texture characteristics of the original image.As compared to Du’s wavelet analysis method and other morphological analysis methods,the approach developed here has reduced artefacts due to factors like hurricane size and has lower programming complexity.In summary,the proposed method provides a new and elegant choice for hurricane eye morphology extraction.