کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4460958 1621363 2006 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Training set size requirements for the classification of a specific class
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
پیش نمایش صفحه اول مقاله
Training set size requirements for the classification of a specific class
چکیده انگلیسی

The design of the training stage of a supervised classification should account for the properties of the classifier to be used. Consideration of the way the classifier operates may enable the training stage to be designed in a manner which ensures that the aim of the classification is satisfied with the use of a small, inexpensive, training set. It may, therefore, be possible to reduce the training set size requirements from that generally expected with the use of standard heuristics. Substantial reductions in training set size may be possible if interest is focused on a single class. This is illustrated for mapping cotton in north-western India by support vector machine type classifiers. Four approaches to reducing training set size were used: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes and the adoption of a one-class classifier. All four approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of ∼ 90% from that suggested by a conventional heuristic are reported with the accuracy of cotton classification remaining nearly constant at ∼ 95% and ∼ 97% from the user's and producer's perspectives respectively.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 104, Issue 1, 15 September 2006, Pages 1–14
نویسندگان
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