Image ProcessingNo Access

Semi-Automatic Extraction and Mapping of Farmlands Based on High-Resolution Remote Sensing Images

Dongsheng Liu

https://orcid.org/0000-0002-4529-5303

School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China

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and

Ling Han

School of Land Engineering, Chang’an University, Xi’an 710054, China

Key Laboratory of Land Consolidation in Shaanxi Province, Xi’an 710054, China

E-mail Address: hanling@chd.edu.cn

Corresponding author.

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https://doi.org/10.1142/S0218001422540027Cited by:4 (Source: Crossref)

Abstract

Extraction of agricultural parcels from high-resolution satellite imagery is an important task in precision agriculture. Here, we present a semi-automatic approach for agricultural parcel detection that achieves high accuracy and efficiency. Unlike the techniques presented in previous literatures, this method is pixel based, and it exploits the properties of a spectral angle mapper (SAM) to develop customized operators to accurately derive the parcels. The main steps of the method are sample selection, textural analysis, spectral homogenization, SAM, thresholding, and region growth. We have systematically evaluated the algorithm proposed on a variety of images from Gaofen-1 wide field of view (GF-1 WFV), Resource 1-02C (ZY1-02C), and Gaofen-2 (GF-2) to aerial image; the accuracies are 99.09% of GF-1 WFV, 84.42% of ZY1-02C, 96.51% and 92.18% of GF-2, and close to 100% of aerial image; these results demonstrated its accuracy and robustness.

Keywords:

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