Improvements in Sample Selection Methods for Image Classification
AbstractTraditional image classification algorithms are mainly divided into unsupervised and supervised paradigms. In the first paradigm, algorithms are designed to automatically estimate the classes’ distributions in the feature space. The second paradigm depends on the knowledge of a domain expert to identify representative examples from the image to be used for estimating the classification model. Recent improvements in human-computer interaction (HCI) enable the construction of more intuitive graphic user interfaces (GUIs) to help users obtain desired results. In remote sensing image classification, GUIs still need advancements. In this work, we describe our efforts to develop an improved GUI for selecting the representative samples needed to estimate the classification model. The idea is to identify changes in the common strategies for sample selection to create a user-driven sample selection, which focuses on different views of each sample, and to help domain experts identify explicit classification rules, which is a well-established technique in geographic object-based image analysis (GEOBIA). We also propose the use of the well-known nearest neighbor algorithm to identify similar samples and accelerate the classification. View Full-Text
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Körting, T.S.; Fonseca, L.M.G.; Castejon, E.F.; Namikawa, L.M. Improvements in Sample Selection Methods for Image Classification. Remote Sens. 2014, 6, 7580-7591.
Körting TS, Fonseca LMG, Castejon EF, Namikawa LM. Improvements in Sample Selection Methods for Image Classification. Remote Sensing. 2014; 6(8):7580-7591.Chicago/Turabian Style
Körting, Thales S.; Fonseca, Leila M.G.; Castejon, Emiliano F.; Namikawa, Laercio M. 2014. "Improvements in Sample Selection Methods for Image Classification." Remote Sens. 6, no. 8: 7580-7591.