MATLAB Implementation Code for Dual-Threshold Segmentation Algorithm in SAR Image Processing

In SAR image processing, the dual-threshold segmentation algorithm serves as a commonly used method. This algorithm effectively partitions images into two distinct threshold regions. The MATLAB implementation involves several key steps: First, the SAR image is loaded using the imread function, which supports various image formats including JPEG, PNG, and TIFF. The code initializes two threshold values (threshold1 and threshold2) that define the segmentation boundaries. The core algorithm utilizes logical indexing to create a binary image. A zero matrix is initialized with the same dimensions as the input image using the zeros function. Pixels with intensity values below threshold1 are assigned a value of 1, while pixels exceeding threshold2 also receive a value of 1. This creates three distinct regions: pixels below threshold1, between thresholds, and above threshold2. The implementation employs vectorized operations for efficient computation, avoiding slow loop structures. The final binary image is displayed using imshow function, which automatically handles contrast scaling for optimal visualization. MATLAB code implementation: % Read input SAR image image = imread('sar_image.jpg'); % Define lower and upper thresholds threshold1 = 100; threshold2 = 200; % Initialize binary image matrix binary_image = zeros(size(image)); % Apply dual-threshold segmentation binary_image(image < threshold1) = 1; binary_image(image > threshold2) = 1; % Display segmentation results imshow(binary_image); By utilizing this code implementation, you can effectively apply dual-threshold segmentation in SAR image processing to partition images into distinct regions based on the defined threshold values. The algorithm demonstrates particular effectiveness in separating different terrain features or target objects in SAR imagery.