Simulated multipolarized MAPSAR images to distinguish agricultural crops

Silva, Wagner Fernando; Rudorff, Bernardo Friedrich Theodor; Formaggio, Antônio Roberto; Paradellá, Waldir Renato; Mura, José Cláudio

doi:10.1590/s0103-90162012000300005

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…With the launch of COSMO-SkyMed 1/2, RADARSAT-2 and Sentinel-1 satellites, multipolarization and fully polarimetric SAR (PolSAR) data have become available in recent years. PolSAR data contain information regarding the shape, dielectric constant, roughness, orientation and backscattering properties of ground objects, thereby having more advantages in crop classification and area monitoring [11][12][13] .…”

Section: Introduction mentioning

confidence: 99%

Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method

Cui

Zhang

Wang

et al. 2020

International Journal of Agricultural and Biological Engineering

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Remote sensing is an important technical means to investigate land resources. Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves, whereas synthetic aperture radar (SAR) imagery is sensitive to changes in growth states and morphological structures. Crop-type mapping with a single type of imagery sometimes has unsatisfactory precision, so providing precise spatiotemporal information on crop type at a local scale for agricultural applications is difficult. To explore the abilities of combining optical and SAR images and to solve the problem of inaccurate spatial information for land parcels, a new method is proposed in this paper to improve crop-type identification accuracy. Multifeatures were derived from the full polarimetric SAR data (GaoFen-3) and a high-resolution optical image (GaoFen-2), and the farmland parcels used as the basic for object-oriented classification were obtained from the GaoFen-2 image using optimal scale segmentation. A novel feature subset selection method based on within-class aggregation and between-class scatter (WA-BS) is proposed to extract the optimal feature subset. Finally, crop-type mapping was produced by a support vector machine (SVM) classifier. The results showed that the proposed method achieved good classification results with an overall accuracy of 89.50%, which is better than the crop classification results derived from SAR-based segmentation. Compared with the ReliefF, mRMR and LeastC feature selection algorithms, the WA-BS algorithm can effectively remove redundant features that are strongly correlated and obtain a high classification accuracy via the obtained optimal feature subset. This study shows that the accuracy of crop-type mapping in an area with multiple cropping patterns can be improved by the combination of optical and SAR remote sensing images.

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Section: Introduction mentioning

confidence: 99%