Fully automated procedure for ship detection using optical satellite imagery

Ship detection from remote sensing imagery is a crucial application for maritime security which includes among others traffic surveillance, protection against illegal fisheries, oil discharge control and sea pollution monitoring. In the framework of a European integrated project GMES-Security/LIMES, we developed an operational ship detection algorithm using high spatial resolution optical imagery to complement existing regulations, in particular the fishing control system. The automatic detection model is based on statistical methods, mathematical morphology and other signal processing techniques such as the wavelet analysis and Radon transform. This paper presents current progress made on the detection model and describes the prototype designed to classify small targets. The prototype was tested on panchromatic SPOT 5 imagery taking into account the environmental and fishing context in French Guiana. In terms of automatic detection of small ship targets, the proposed algorithm performs well. Its advantages are manifold: it is simple and robust, but most of all, it is efficient and fast, which is a crucial point in performance evaluation of advanced ship detection strategies.

[1]  Hugues Talbot,et al.  Mathematical morphology: A useful set of tools for image analysis , 2000, Stat. Comput..

[2]  Naoki Suzuki,et al.  Benchmarking operational SAR ship detection , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[3]  Robert E. Tarjan,et al.  Efficiency of a Good But Not Linear Set Union Algorithm , 1972, JACM.

[4]  Naouma Kourti,et al.  FINDINGS OF THE DECLIMS PROJECT - DETECTION AND CLASSIFICATION OF MARINE TRAFFIC FROM SPACE , 2006 .

[5]  Philippe Salembier,et al.  Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval , 2000, IEEE Trans. Image Process..

[6]  P. Vachon,et al.  Improved ship detection with airborne polarimetric SAR data , 2005 .

[7]  Philippe Salembier,et al.  Antiextensive connected operators for image and sequence processing , 1998, IEEE Trans. Image Process..

[8]  Yichen Tian,et al.  Illicit Vessel Identification In Inland Waters using SAR Image , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[9]  M. Mcdonnell,et al.  SHIP DETECTION FROM LANDSAT IMAGERY , 1978 .

[10]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  J. W. Modestino,et al.  Flat Zones Filtering, Connected Operators, and Filters by Reconstruction , 1995 .

[12]  Michael Inggs,et al.  Ship target recognition using low resolution radar and neural networks , 1999 .

[13]  Ronald Jones,et al.  Connected Filtering and Segmentation Using Component Trees , 1999, Comput. Vis. Image Underst..

[14]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[15]  Michel Couprie,et al.  Building the Component Tree in Quasi-Linear Time , 2006, IEEE Transactions on Image Processing.

[16]  Michel Petit,et al.  Using SPOT–5 HRG Data in Panchromatic Mode for Operational Detection of Small Ships in Tropical Area , 2008, Sensors.

[17]  Jordi Joan Mallorquí Franquet,et al.  Automatic vessel monitoring with single and multidimensional SAR images in the wavelet domain , 2006 .

[18]  D. Crisp,et al.  The State-of-the-Art in Ship Detection in Synthetic Aperture Radar Imagery , 2004 .

[19]  D. W. Burgess Automatic ship detection in satellite multispectral imagery , 1993 .

[20]  Paolo Cipollini,et al.  Propagation characteristics of extratropical planetary waves observed in the ATSR global sea surface temperature record , 2000 .

[21]  Carlos López-Martínez,et al.  A NOVEL ALGORITHM FOR SHIP DETECTION IN ENVISAT SAR IMAGERY BASED ON THE WAVELET TRANSFORM , 2004 .

[22]  Enrico Magli,et al.  Pattern recognition by means of the Radon transform and the continuous wavelet transform , 1999, Signal Process..