Identification of paddy fields in Northern Japan using RapidEye images

Ichikawa, D.; Wakamori, K.; Suzuki, Mitsuo

doi:10.1109/igarss.2014.6946877

Cited by 7 publications

(1 citation statement)

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“…The vegetation index used in our VIT method was Normalized Difference Vegetation Index (NDVI) [48]. The VIT only needs the NDVI histogram of the target-class training set and an empirical percentage (δ) to decide the thresholds [49]. If the NDVI value is out of head and tail percentages determined by the thresholds, the pixel is excluded.…”

Section: Classification Methodsmentioning

confidence: 99%

Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery

Jiang

et al. 2018

Remote Sensing

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Identification of paddy fields is essential for monitoring the rice cultivated area and predicting rice productivity. Timely and accurate extraction of rice distribution can bring vital information for national food security, agricultural policy formulation, and regional environmental sustainability. Conventional classification methods usually suffered from low accuracy, multi-class training samples, or demanding imagery requirements. This paper proposes to use one-class support vector classification (OCSVC) to extract rice cultivated area with Landsat Optical Land Imager (OLI) imagery. Instead of sampling and training all land cover types as performed by multi-class classification methods, OCSVC only used the training samples of target class (rice) for rice mapping. The performance of OCSVC was evaluated in terms of the classification accuracy of rice mapping and rice acreage estimation based on high-resolution imagery, field survey data and rice acreage data from government reports for Jiangsu Province, China. At the county-level, OCSVC was also compared with the commonly used multi-class support vector classification (MCSVC), decision tree classification (DTC), and vegetation index-based thresholding (VIT). Our results demonstrated that OCSVC produced a comparable overall accuracy to DTC and outperformed MCSVC and VIT. The computational efficiency of OCSVC increased approximately ten times as compared to MCSVC. The OCSVC produced the best correlation between its classified area and reported area among the four classification methods evaluated. When applied to the provincial level, the classification overall accuracy for OCSVC was 88.54%. The detected rice planting area for Jiangsu Province was 22,602 km 2 , which was consistent with the statistics from the National Bureau of Statistics (22,948 km 2 ). This OCSVC-based mapping strategy provides a practical and efficient way to detect the rice planting extent with Landsat imagery at a large scale.

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Section: Classification Methodsmentioning

confidence: 99%