Title
Supervised landmask estimation using contextual information in SAR data.
Abstract
Synthetic Aperture Radars are powerful observation tools in cases where the utilization of optical data is restricted. As one of the main applications of these systems is the control of maritime traffic, a land mask needs to be estimated. In this paper two different processing schemes are proposed in order to perform the land mask estimation on a TerraSAR-X acquired SAR image. The first one consists on an unsupervised edge detector based on the wavelet transform modulus maxima, while the second one performs a supervised detection based on SVMs. Both processing schemes apply a blocktracing algorithm after the edge detection stage. The edge detector based on the wavelet transform finds quite a lot of edges over the sea area, missclassifying a big region of water as land. Thanks to contextual information and the supervised training, the edge detector based on SVMs can outperform the edge detector based on the wavelet transform in the classification of sea areas obtaining a better landmask.
Year
DOI
Venue
2012
10.1109/IVS.2012.6232297
Intelligent Vehicles Symposium
Keywords
Field
DocType
edge detection,image classification,marine control,radar imaging,remote sensing by radar,support vector machines,synthetic aperture radar,traffic control,wavelet transforms,SAR image,SVM,TerraSAR-X,blocktracing algorithm,contextual information,maritime traffic control,observation tool,optical data utilization,sea area classification,supervised detection,supervised landmask estimation,supervised training,synthetic aperture radar,unsupervised edge detector,wavelet transform modulus maxima
Computer vision,Radar imaging,Pattern recognition,Edge detection,Computer science,Synthetic aperture radar,Support vector machine,Image segmentation,Artificial intelligence,Contextual image classification,Detector,Wavelet transform
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
5