Recent years have seen an increased use of Unmanned Aerial Vehicles (UAV) with video-recording capability for Maritime Domain Awareness (MDA) and other surveillance operations. In order for these e orts to be effective, there is a need to develop automated algorithms to process the full-motion videos (FMV) captured by UAVs in an efficient and timely manner to extract meaningful information that can assist human analysts and decision makers. This paper presents a generalizeable marine object detection system that is specifically designed to process raw video footage streaming from UAVs in real-time. Our approach does not make any assumptions about the object and/or background characteristics because, in the MDA domain, we encounter varying background and foreground characteristics such as boats, bouys and ships of varying sizes and shapes, wakes, white caps on water, glint from the sun, to name but a few. Our efforts rely on basic signal processing and machine learning approaches to develop a generic object detection system that maintains a high level of performance without making prior assumptions about foreground-background characteristics and does not experience abrupt performance degradation when subjected to variations in lighting, background characteristics, video quality, abrupt changes in video perspective, size, appearance and number of the targets. In the following report, in addition to our marine object detection system, we present representative object detection results on some real-world UAV full-motion video data.
In this paper we discuss an algorithm developed to detect re in high-resolution commercial multispectral imagery.
We discuss the utility of such algorithms and present to the reader the challenges in developing these types of
algorithms. A thorough description of the algorithm is presented along with the results of its experimental
Southern California experienced some of the largest wildfires ever seen in 2003 and 2007. The Cedar fire in 2003
resulted in 2,820 lost structures and 15 deaths, and the Witch fire in 2007 resulted in 1,650 lost structures and 2 deaths
according to the California Department of Forestry and Fire Protection (CAL FIRE). Fighting fires of this magnitude
requires every available resource, and an adequate water supply is vital in the firefighting arsenal. Utilizing the fact that
many homes in Southern California have swimming pools, firefighters could have access to strategically placed water
supplies. The problem is accurately and quickly identifying which residences have actively filled swimming pools at the
time of the emergency. The proposed method approaches the problem by employing satellite imagery and remote
sensing techniques. Specifically, swimming pool identification is attempted with Spectral Angle Mapper (SAM) on
multispectral imagery from the Worldview-2 satellite.
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