Support Vector Machine (SVM)-Based Wharf Detection Algorithm

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Resource Overview

A wharf detection algorithm leveraging Support Vector Machines (SVM) designed for high-resolution remote sensing imagery, incorporating image preprocessing and feature extraction techniques.

Detailed Documentation

A Support Vector Machine (SVM)-based wharf detection algorithm designed for high-resolution remote sensing imagery enables effective object detection and recognition. By integrating SVM models with image processing and machine learning techniques, this algorithm accurately locates and extracts wharf targets from remote sensing images while performing precise classification and identification. Through systematic analysis and processing of imagery, the algorithm identifies key features such as wharf location, shape, and dimensions. Implementation typically involves preprocessing steps like image normalization and feature extraction using Histogram of Oriented Gradients (HOG) or texture descriptors, followed by SVM training with radial basis function (RBF) kernels for nonlinear classification. This approach provides robust support for urban planning, port management, and marine resource development applications.

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