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Detections and classification of non-AIS-compliant vessels is an important ability for countries or institutions interested in MDA. SAR has been proven to be an effective method but there exists a trade-off between the area that can be imaged and the resolution of each image pixel. Large swath SAR images are a cost effective method of performing maritime surveillance but classification or identification...
An automatic approach to detect bilge dumping in synthetic aperture radar (SAR) images over Southern African oceans is proposed. The approach uses a threshold-based algorithm and a region-based active contour model (ACM) algorithm to achieve an efficient bilge dump detection tool. A threshold method was used to detect areas with a high bilge dump probability while the ACM method is used to get closed...
Regional monitoring of land cover conversion of natural vegetation to new informal human settlements is essential when investigating the migration of people to urbanized cities. Detecting these new settlements require reliable change detection methods. A robust change detection metric can be derived by analyzing the area under the autocorrelation function for a time series. The time dependence on...
A probability graph model can effectively model spectral and spatial dependencies within remote sensing images for land cover classification. The most common structure used to unify this probabilistic information is a second order Markov network that encapsulate unary and pairwise potentials. In this paper we explore various heuristics to discover new graph structures that will assist with classifying...
The widespread access to Synthetic Aperture Radar data has created a need for more precise ship extraction, specifically in low-to-medium resolution imagery. While Synthetic Aperture Radar pixel resolution is improving for a large swaths, information about ships from within the Synthetic Aperture Radar intensity imagery is still sparse. Ships that are a few pixels across provide little information...
Efficient and effective ship discrimination across multiple Synthetic Aperture Radar sensors is becoming more important as access to SAR data becomes more widespread. A flexible means of separating ships from sea is ideal and can be accomplished using machine learning. Newer, advanced deep learning techniques offer a unique solution but traditionally require a large dataset to train effectively. Highway...
This paper shows the utility of Synthetic Aperture Radar (SAR) data acquired from the recently launched Sentinel-1 satellite for the detection of vessels within the South African Exclusive Economic Zone (EEZ). A Sentinel-1 dual polarized (HH and HV) image acquired in Extra Wide (EW) mode over two of the busiest harbors in the country was used together with a standard Cell averaging Constant False...
In this paper we present a 2-tier higher order Conditional Random Field which is used for land cover classification. The Conditional Random Field is based on probabilistic messages being passed along a graph to compute efficiently the conditional probability for a land cover class. Conventionally the information is passed among direct spatial neighbors to improve classification accuracy. The inclusion...
Regular surveillance of territorial sea areas is increasingly important for coastal nations as these marine areas are a valuable economic resource (e.g. for fisheries or oil extraction). The responsibility for the administration, law enforcement, environmental protection and sustainable management of this frontier can be very challenging as systematic surveillance of these areas is very costly and...
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