论文信息 - Synthetic Aperture Radar Image Processing using the Supervised Textural-Neural Network Classification Algorithm

Synthetic Aperture Radar Image Processing using the Supervised Textural-Neural Network Classification Algorithm

Synthetic Aperture Radar (SAR) satellite images have proven to be a successful tool for identifying oil slicks. Natural oil seeps can be detected as elongated, radar-dark slicks in SAR images. Use of SAR images for seep detection is enhanced by a Texture Classifying Neural Network Algorithm (TCNNA), which delineates areas where layers of floating oil suppress Bragg scattering. The effect is strongly influenced by wind strength and sea state. A multi orientation Leung-Malik filter bank [1] is used to identify slick shapes under projection of edges. By integrating ancillary data consisting of the incidence angle, descriptors of texture and environmental variables, considerable accuracy were added to the classification ability to discriminate false targets from oil slicks and look-alike pixels. The reliability of the TCNNA is measured after processing 71 images containing oil slicks.

[1] Carlos Henrique Beisl,et al. Analysis of RADARSAT-1 data for offshore monitoring activities in the Cantarell Complex, Gulf of Mexico, using the unsupervised semivariogram textural classifier (USTC) , 2004 .

[2] J. Fortuny-Guasch,et al. Improved Oil Slick Detection and Classification with Polarimetric SAR , 2003 .

[3] K. Topouzelis,et al. OIL SPILL DETECTION USING RBF NEURAL NETWORKS AND SAR DATA , 2004 .

[4] I. MacDonald,et al. Distinct side-scan sonar, RADARSAT SAR, and acoustic profiler signatures of gas and oil seeps on the Gulf of Mexico slope , 2003 .

[5] I. Leifer,et al. Transfer of hydrocarbons from natural seeps to the water column and atmosphere , 2002 .

[6] J. Amos,et al. Remote Sensing Inventory of Active Oil Seeps and Chemosynthetic Communities in the Northern Gulf of Mexico , 1996 .

[7] Jitendra Malik,et al. Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[8] H. Roberts,et al. Seafloor reflectivity—An important seismic property for interpreting fluid/gas expulsion geology and the presence of gas hydrate , 2006 .