Robust Anomaly-Based Ship Proposals Detection from Pan-sharpened High-Resolution Satellite Image

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 anomaly and local texture anomaly map. Regarding noise appeared due to the present of sea clutter and because of pan-sharpen process, texture abnormality suppression term based on quantization theory is introduced. Experimental results on VNREDSat-1 VHR optical satellite images suggest that the pan-sharpened near-infrared (P-NIR) band can improve discrimination of ships from surrounding waters. Compared to state-of-the-art anomaly-based detectors, our proposed anomaly-based model on the combination of PAN and P-NIR data cannot only achieved highest ship detection's recall rate (91.14% and 45.9% on high-contrast and low-contrast dataset respectively) but also robust to different automatic threshold selection techniques.