Hierarchical image detection techniques based on high-resolution remote sensing data

Hierarchical image detection in high-resolution remote sensing utilizes a structured approach to address the challenges posed by varying target sizes and proportions. The process begins with segmenting the target foreground using a hierarchical Gaussian mixture model. This is followed by employing texture constraints between pixels within the target foreground, aided by least squares support vector machines (LSSVM) for initial target detection. Next, attribute constraints between neighbouring objects help correct initial detection errors. Finally, spatial distribution constraints and category characteristic distribution constraints refine the results, addressing mislabelling at the hierarchical level. Experimental results demonstrate that this method effectively classifies ground objects and detects vehicle targets in high-resolution remote sensing images, enhancing detection accuracy amidst spatial and semantic complexities.