On the Use of Feature Selection for Classifying Multitemporal Radarsat-1 Images for Forest Mapping

• Published in: IEEE geoscience and remote sensing letters Vol. 8; no. 5; pp. 904 - 908
• Main Authors: Maghsoudi, Y., Collins, M. J., Leckie, D.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Availability; Classification; Feature extraction; Forestry; Forests; Mapping; Remote sensing; Scattering; Spatial resolution; Speckle; Synthetic aperture radar; synthetic aperture radar (SAR); time series analysis
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2011.2140311

As the number of satelliteborne SAR systems increases, both the availability and the length of multitemporal (MT) sequences of SAR images have also increased. Reported research with MT SAR sequences suggests that they increase the classification accuracy for all applications over single-date images. The length of the MT SAR sequences reported in the literature is still quite modest: on the order of six images. As the length of a sequence increases, the selection of images to use in a classification becomes important. The current practice is to add scenes chronologically, and some researchers have suggested that image selection does not affect classification accuracy. Our research explored the problem of image selection in MT SAR classification. We compared the chronological selection scheme with two feature selection algorithms: a very simple algorithm and a more complex class-based algorithm. We found that, while the simple feature selection algorithm was more efficient than chronological selection, yielding peak accuracy with few features, it saturated at the same accuracy as chronological selection. The more complex algorithm was significantly more accurate than chronological selection, even with just two features. Our results suggest that the use of a feature selection algorithm produces more efficient and more accurate classification results than chronological selection.