  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.
