Gradient convolutional neural network for classification of agricultural fields with contour levee

ABSTRACT

To better understand how contour-levee irrigation practice impacts water resources for formulating effective water management policies, it is important to obtain its application on large-scale data sets, e.g. state-wide. Automatic classification of contour levee croplands from high-resolution aerial images is of great potential given the success of deep neural networks and the availability of high-resolution remote sensing imagery. This paper proposes a gradient CNN model to classify fields with contour levees from remote sensing images. Our model produces high-quality segmentation masks that are refined with superpixel-based segmentation post-processing. Our method is evaluated using images by the National Agriculture Imagery Program (NAIP) for the counties in Arkansas. A comparison with the state-of-the-art methods demonstrates the improved performance of our proposed method. Our method demonstrates superior performance in the classification of challenging cases and achieves an overall 3.08% of accuracy improvement and 28.57% BER error reduction, compared to the second-best method. The p-value with respect to the second-best method is 0.005, which indicates great statistical significance. In addition, the results for data of different counties demonstrate the exceptional generalization ability of our method.

Acknowledgements

We appreciate the comments and assistance by George Mihaila and Jawad Ahmed in the preparation of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

Data sharing is not applicable to this article as no new data were created or analysed in this study.