In the authors' previous work, a sequence of image-processing algorithms was developed that was suitable for detecting
and classifying ships from panchromatic Quickbird electro-optical satellite imagery. Presented in this paper are several
new algorithms, which improve the performance and enhance the capabilities of the ship detection software, as well as
an overview on how land masking is performed.
Specifically, this paper describes the new algorithms for enhanced detection including for the reduction of false detects
such as glint and clouds. Improved cloud detection and filtering algorithms are described as well as several texture
classification algorithms are used to characterize the background statistics of the ocean texture. These detection
algorithms employ both cloud and glint removal techniques, which we describe. Results comparing ship detection with
and without these false detect reduction algorithms are provided.
These are components of a larger effort to develop a low-cost solution for detecting the presence of ships from readily-available
overhead commercial imagery and comparing this information against various open-source ship-registry
databases to categorize contacts for follow-on analysis.
This paper presents a sequence of image-processing algorithms suitable for detecting and classifying ships from nadir
panchromatic electro-optical imagery. Results are shown of techniques for overcoming the presence of background sea
clutter, sea wakes, and non-uniform illumination. Techniques are presented to measure vessel length, width, and
direction-of-motion. Mention is made of the additional value of detecting identifying features such as unique
superstructure, weaponry, fuel tanks, helicopter landing pads, cargo containers, etc.
Various shipping databases are then described as well as a discussion of how measured features can be used as search
parameters in these databases to pull out positive ship identification. These are components of a larger effort to develop a
low-cost solution for detecting the presence of ships from readily-available overhead commercial imagery and
comparing this information against various open-source ship-registry databases to categorize contacts for follow-on
An automatic target recognition algorithm for synthetic aperture radar (SAR) imagery data is developed. The algorithm classifies an unknown target as one of the known reference targets based on a maximum likelihood estimation procedure. The algorithm helps assess and optimize the favorable effects of multiple image features on recognition accuracy. This study addresses four procedures: (1) feature extraction, (2) training set creation, (3) classification of unknown images, and (4) optimization of recognition accuracy. A three-feature probabilistic method based on extracted edges, corners, and peaks is used to classify the targets. Once the three features are extracted from the target image, binary images are created from each. Training sets, which are used to classify an unknown target, are then created using average Hausdorff distance values for each of the known members of the eight target image types (ZSU-23-4, ZIL131, D7, 2S1, SLICY, BDRM2, BTR60, and T62) included in the publicly available MSTAR test data. The average Hausdorff distance values are acquired from unknown target feature images and are compared to each training set. Each comparison provides the likelihood of the unknown target belonging to one of the eight possible known targets. For each target, eight likelihoods (for eight possible unknown targets) are determined based on the Hausdroff distances and the pre-assigned feature weights. The unknown target is then classified into the target type that has the maximum likelihood estimation value.