Crop classification with WorldView-2 imagery using Support Vector Machine comparing texture analysis approaches and grey relational analysis in Jianan Plain, Taiwan

Abstract

Crop production estimation is of crucial concern in Taiwan, and the government has invested much effort including employing manpower and technologies. Artificial intelligence or data mining technology have been successfully applied on land-cover recognition of remote-sensing imagery. Most studies are employing a pixel-based classification approach to generate the thematic map of land covers. Few studies consider the spatial correlation of adjacent pixels of the same category. Mixed pixel issues usually degrade the prediction accuracy according to previous studies. It is thus the main goal of the present study to explore the spatial effect of adjacent pixels on land-cover mapping. The study region is a WorldView-2 satellite image in Jianan Plain, Taiwan, taken in 2014. Support Vector Machine is used as the underlying classifier. In addition to the eight spectral band intensities, normalized difference vegetation index and grey-level co-occurrence matrix (GLCM) textures are included as ancillary attributes. Furthermore, grey relational analysis (GRA) is employed to assist in classifying croplands. The findings of this study can be summarized as follows: (1) GLCM texture information improves classification accuracy marginally and renders slightly better thematic map, (2) GRA is used to acquire the most important factors concerning discriminating land covers, (3) grey relational grade threshold, a metric designed through GRA, can be used to locate uncertain region of a specified crop, which is possibly caused by mixed pixels.