Robust Anomaly-Based Ship Proposals Detection from Pan-sharpened High-Resolution Satellite Image
Viet Hung Luu, Nguyen Hoang Hoa Luong, Quang Hung Bui, and Thi Nhat, Thanh Nguyen

TL;DR
This paper enhances anomaly-based ship detection in high-resolution satellite images by integrating pan-sharpened multi-spectral data and texture suppression, achieving higher recall and robustness over existing methods.
Contribution
It introduces a novel anomaly detection model combining intensity and texture anomalies with pan-sharpened MS data, improving detection accuracy and threshold robustness.
Findings
Achieved 91.14% recall on high-contrast datasets.
Improved detection in low-contrast scenarios.
Enhanced robustness to threshold selection.
Abstract
Pre-screening of ship proposals is now employed by top ship detectors to avoid exhaustive search across image. In very high resolution (VHR) optical image, ships appeared as a cluster of abnormal bright pixels in open sea clutter (noise-like background). Anomaly-based detector utilizing Panchromatic (PAN) data has been widely used in many researches to detect ships, however, still facing two main drawbacks: 1) detection rate tend to be low particularly when a ship is low contrast and 2) these models require a high manual configuration to select a threshold value best separate ships from sea surface background. This paper aims at further investigation of anomaly-based model to solve those issues. First, pan-sharpened Multi Spectral (MS) data is incorporated together with PAN to enhance ship discrimination. Second, we propose an improved anomaly-based model combining both global intensity…
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Taxonomy
TopicsRemote-Sensing Image Classification · Identification and Quantification in Food · Spectroscopy Techniques in Biomedical and Chemical Research
