Abstract
High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. Simple Non-Iterative Clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.36%, respectively, when compared with the pixel-based classification, based on the combination of optical and SAR data (using the random forest as the classifier). The results show that SNIC superpixel segmentation is a feasible method for high-resolution crop mapping based on multi-source remote sensing data. The automatic selection of the optimal superpixel size of SNIC will be focused on in future research.
Highlights
A high-resolution GF-1 pan-sharpened image was partitioned into multiple superpixels based on the Simple Non-Iterative Clustering (SNIC) method and the features of each superpixel were calculated by averaging the values of all pixels of multi-source time-series medium resolution images contained in the superpixel, to carry out rice mapping in the study area based on a random forest (RF) classifier
Sentinel-2 MSI (S2) + vertical received (VV) + VH is better than that of S2 optical images alone. This is mainly because June and July were the key periods of rice transplanting in this area, as well as the critical phehard to integrate the information from coarse resolution optical images and synthetic aperture radar (SAR) images, nological period of rice mapping
A 2 m resolution GF-1 pan-sharpened image was segmented into superpixels based on the SNIC algorithm and used for rice mapping based on time-series Sentinel-2
Summary
High-resolution satellite images can provide a more precise and accurate spatial distribution of crop planting plots, providing important reference information for the implementation of precision agriculture and the formulation of agricultural policies [1,2]. Crop mapping based on high-resolution satellite images is restricted by the difficulties in obtaining time-series high-resolution satellite data and appropriate classification methods [3]. High-resolution satellite imagery can provide clearer and richer ground details, providing advantages for crop mapping.
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