Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network

Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network

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Using satellite remote sensing has become a mainstream approach for extracting crop spatial distribution.Making edges finer is a challenge, while simultaneously extracting crop spatial distribution information from high-resolution remote sensing images using a convolutional neural network (CNN).Based on the characteristics of the crop area in the Gaofen 2 (GF-2) images, this paper proposes an improved CNN to Baby Cream extract fine crop areas.The CNN comprises a feature extractor and a classifier.The feature extractor employs a spectral feature extraction unit to generate spectral features, and five coding-decoding-pair units to generate five level features.

A linear model is used to fuse features of different levels, and the fusion results are up-sampled to Massage Oils obtain a feature map consistent with the structure of the input image.This feature map is used by the classifier to perform pixel-by-pixel classification.In this study, the SegNet and RefineNet models and 21 GF-2 images of Feicheng County, Shandong Province, China, were chosen for comparison experiment.Our approach had an accuracy of 93.26%, which is higher than those of the existing SegNet (78.

12%) and RefineNet (86.54%) models.This demonstrates the superiority of the proposed method in extracting crop spatial distribution information from GF-2 remote sensing images.