Target detection based on multiresolution fractal analysis

Abstract

SAR imaging has been extensively used in several applications including automatic target detection and recognition. In this paper, a wavelet/fractal (WF)-based target detection technique is presented. The technique computes a fractal-based feature on an edge image, as opposed to existing fractal methods that compute the fractal dimension on the original image. The edge image is produced through the use of wavelets. The technique is evaluated for target detection in SAR images, and compared with a previous fractal-based approach, namely the extended fractal (EF) model. Experimental results illustrate that WF provides lower false alarm rates for the same probability of detection compared to EF. Furthermore, it is shown that WF provides higher spatial resolution capabilities for the detection of closely located targets.

Wavelet thresholding and F-NLM filtering based denoising algorithms applied to high resolution SAR ship detection

“…The application of SAR ship data for the detection of ship targets on the water will be able to effectively enhance the early warning capability of sea defense, strengthen the detection and management of fisheries resources, as well as possess an extensive range of application prospects along with vital significance for national development 2,3 . At the current stage, there are four predominant methods for target detection based on SAR images: Target detection methods based on structural features [4][5][6] , grey-scale features [7][8][9] , texture features [10][11][12][13] , and deep learning [14][15][16][17] . In comparison, deep learning-based methods boast powerful feature extraction capabilities and are capable of automatically learning structured features to successfully achieve high-precision recognition of detection targets [18][19][20] .…”

Target detection method based quadtree structure search and Hurst exponent

“…It has been shown that the fractal dimension has a strong correlation with human judgment of surface roughness [1]. The existing target detection and image segmentation methods include fractal coding [2], local fractal dimension [3], directional fractal dimension [4,5], extended fractal analysis [6], multifractal analysis [7,8] and others such as combining with wavelet [9]and neural network etc.…”