Back to Search
Start Over
Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network.
- Source :
- Applied Sciences (2076-3417); 7/15/2019, Vol. 9 Issue 14, p2917, 19p
- Publication Year :
- 2019
-
Abstract
- 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 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 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- REMOTE sensing
CROPS
FEATURE extraction
WINTER wheat
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 9
- Issue :
- 14
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 137799110
- Full Text :
- https://doi.org/10.3390/app9142917