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http://dx.doi.org/10.7780/kjrs.2017.33.5.2.8

Crop Classification for Inaccessible Areas using Semi-Supervised Learning and Spatial Similarity - A Case Study in the Daehongdan Region, North Korea -  

Kwak, Geun-Ho (Department of Geoinformatic Engineering, Inha University)
Park, No-Wook (Department of Geoinformatic Engineering, Inha University)
Lee, Kyung-Do (Climate Change and Agroecology Division, National Institute of Agricultural Sciences)
Choi, Ki-Young (Agricultural and Fisheries Statistics Division, Population & Social Statistics Bureau, Statistics Korea)
Publication Information
Korean Journal of Remote Sensing / v.33, no.5_2, 2017 , pp. 689-698 More about this Journal
Abstract
In this paper, a new classification method based on the combination of semi-supervised learning with spatial similarity of adjacent pixels is presented for crop classification in inaccessible areas. Iterative classification based on semi-supervised learning is applied to extract reliable training data from both the initial classification result with a small number of training data, and classification results of adjacent pixels are also considered to extract new training pixels with less uncertainty. To evaluate the applicability of the proposed method, a case study of the classification of field crops was carried out using multi-temporal Landsat-8 OLI acquired in the Daehongdan region, North Korea. From a case study, the misclassification of crops and forests, and isolated pixels in the initial classification result were greatly reduced by applying the proposed semi-supervised learning method. In addition, the combination of classification results of adjacent pixels for the extraction of new training data led to the great reduction of both misclassification results and isolated pixels, compared to the initial classification and traditional semi-supervised learning results. Therefore, it is expected that the proposed method would be effectively applied to classify areas in which it is difficult to collect sufficient training data.
Keywords
Classification; Crop; North Korea; Semi-supervised learning;
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Times Cited By KSCI : 3  (Citation Analysis)
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