SHIP DETECTION:
SHIP
detection in space borne remote sensing images is of vital importance
for maritime security and other applications, e.g., traffic
surveillance, protection against illegal fisheries, oil discharge
control, and sea pollution monitoring.
Vessel
monitoring from satellite images provides a wide visual field and
covers large sea area and thus achieves a continuous monitoring of
vessels’ locations and movements.
It
is also known that optical spaceborne images have higher resolution and
more visualized contents than other remote sensing images, which is
more suitable for ship detection or recognition in the aforementioned
applications.
However,
optical spaceborne images usually suffer from two main issues: 1)
weather conditions like clouds, mists, and ocean waves result in more
pseudotargets for ship detection, and 2) optical spaceborne images with
higher resolution naturally lead to larger data quantity than other
remote sensing images, and thus, optical spaceborne images are more
difficult to be tackled for real-time applications.
The
ELM(Extreme Learning Machine), is adopted for feature fusion and
classification, and thus, faster and better ship detection is achieved.
Using these novel techniques, the proposed framework is more suitable
for ship detection than the aforementioned approaches with the following
advantages.
1) Faster detection. Compressed domain achieves much faster detection than pixel domain.
2) More reliable results. High-level feature representations are extracted by hierarchical deep architecture to ensure more accurate classification.
3) Better utilization of information. Two DNNs are trained with multisubbands coefficients to make full use of the wavelet information.
FOR 2015 IEEE PAPER ON SHIP DETECTION USING IMAGE PROCESSING
