Aerial video surveillance has advanced significantly in recent years, as inexpensive high-quality video cameras and airborne platforms are becoming more readily available. Video has become an indispensable part of military operations and is now becoming increasingly valuable in the civil and paramilitary sectors. Such surveillance capabilities are useful for battlefield intelligence and reconnaissance as well as monitoring major events, border control and critical infrastructure. However, monitoring this growing flood of video data requires significant effort from increasingly large numbers of video analysts. We have developed a suite of aerial video exploitation tools that can alleviate mundane monitoring from the analysts, by detecting and alerting objects and activities that require analysts’ attention. These tools can be used for both tactical applications and post-mission analytics so that the video data can be exploited more efficiently and timely. A feature-based approach and a pixel-based approach have been developed for Video Moving Target Indicator (VMTI) to detect moving objects at real-time in aerial video. Such moving objects can then be classified by a person detector algorithm which was trained with representative aerial data. We have also developed an activity detection tool that can detect activities of interests in aerial video, such as person-vehicle interaction. We have implemented a flexible framework so that new processing modules can be added easily. The Graphical User Interface (GUI) allows the user to configure the processing pipeline at run-time to evaluate different algorithms and parameters. Promising experimental results have been obtained using these tools and an evaluation has been carried out to characterize their performance.
In this paper, we propose and illustrate a methodology for classifying the change detection results generated from repeatpass
polarimetric RADARSAT-2 images and segmenting only the changes of interest to a given user while suppressing
all other changes. The detected changes are first classified based on generated supervised ground-cover classification of
the polarimetric SAR images between which changes were detected. In the absence of reliable ground truth needed for
generating supervised classification training sets, we rely on the use of periodically acquired high-resolution, multispectral
optical imagery in order to classify the manually selected training sets before computing their classes' statistics
from the SAR images. The classified detected changes can then be segmented to isolate the changes of interest, as
specified by the user and suppress all other changes. The proposed polarimetric change detection, classification and
segmentation method overcomes some of the challenges encountered when visualizing and interpreting typical raw
change results. Often these non-classified change detection results tend to be too crowded, as they show all the changes
including those of interest to the user as well as other non-relevant changes. Also, some of the changes are difficult to
interpret, especially those which are attributed to a mixture of the backscatters. We shall illustrate how to generate,
classify and segment polarimetric change detection results from two SAR images over a selected region of interest.
Tracking the progress and impact of large scale projects in areas of active conflict is challenging. In early 2010, the
Canadian International Development Agency (CIDA) broke ground on an ambitious project to rehabilitate a network of
just under 600 km of canals that supply water from the Arghandab River throughout southern Kandahar Province
thereby restoring a reliable and secure water supply and stimulating a once vibrant agricultural region. Monitoring the
region for signs of renewal is difficult due to the large areal extent of the irrigated land and safety concerns. With the
support of the Canadian Space Agency, polarimetric change detection techniques are applied to space-borne SAR data to
safely monitor the area through a time-series of RADARSAT-2 images acquired during the rehabilitation ground work
and subsequent growing seasons. Change detection maps delineating surface cover improvement will aid CIDA in
demonstrating the positive value of Canada's investment in renovating Afghanistan's irrigation system to improve water
distribution. This paper examines the use of value-added SAR imaging products to provide short- and long-term
monitoring suitable for assessing the impact and benefit of large scale projects and discusses the challenges of
integrating remote sensing products into a non-expert user community.
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