Abstract
This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce “multi-scale time series feature similarity indicators”. We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index.
Highlights
Paddy rice takes up the largest crop area and has great significance for the global economy, society, and culture
This study developed a multi-scale time series feature similarity index through the Dynamic Time Wrapping (DTW) theory to integrate multi-source scale time-series image information
The training/test dataset was analyzed through a verification process proving that the original feature information was added to the time series similarity index of the multi-scale time series feature data
Summary
Paddy rice takes up the largest crop area and has great significance for the global economy, society, and culture. Farmland surveys in various countries are mainly manual surveys, which are time-consuming, labor-intensive, and extremely inefficient. With the advancement and development of satellite remote sensing detection technology in recent years, farmland monitoring methods have become well-accepted. The use of satellite image data as a monitoring tool along with the use of the machine learning approach has become a major solution for land cover measurements. This includes supervised learning and unsupervised learning in machine learning. This greatly reduces the manpower and material resources required for agricultural monitoring and management [1,2]
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