Hierarchy in Picture Segmentation: A Stepwise Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
A versatile technique for visual enhancement of medical ultrasound images
Digital Signal Processing
IRGS: Image Segmentation Using Edge Penalties and Region Growing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum description length synthetic aperture radar image segmentation
IEEE Transactions on Image Processing
Amplitude vs intensity Bayesian despeckling in the wavelet domain for SAR images
Digital Signal Processing
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This paper presents an algorithm to segment synthetic aperture radar (SAR) images, corrupted by speckle noise. Most standard segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, i.e. without any preprocessing step. The algorithm includes a statistical region growing procedure combined with hierarchical region merging. The region growing step oversegments the input radar image, thus enabling region aggregation by employing a combination of the Kolmogorov-Smirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for performance improvement. We have tested and assessed the proposed technique on artificially speckled image and real SAR data.