Version 1
: Received: 27 December 2017 / Approved: 28 December 2017 / Online: 28 December 2017 (02:54:12 CET)
How to cite:
Zhang, C.; Wan, S.; Gao, S.; Yu, F.; Wei, Q.; Wang, G.; Cheng, Q.; Song, D. A Segmentation Model for Extracting Farmland and Woodland from Remote Sensing Image. Preprints2017, 2017120192. https://doi.org/10.20944/preprints201712.0192.v1
Zhang, C.; Wan, S.; Gao, S.; Yu, F.; Wei, Q.; Wang, G.; Cheng, Q.; Song, D. A Segmentation Model for Extracting Farmland and Woodland from Remote Sensing Image. Preprints 2017, 2017120192. https://doi.org/10.20944/preprints201712.0192.v1
Zhang, C.; Wan, S.; Gao, S.; Yu, F.; Wei, Q.; Wang, G.; Cheng, Q.; Song, D. A Segmentation Model for Extracting Farmland and Woodland from Remote Sensing Image. Preprints2017, 2017120192. https://doi.org/10.20944/preprints201712.0192.v1
APA Style
Zhang, C., Wan, S., Gao, S., Yu, F., Wei, Q., Wang, G., Cheng, Q., & Song, D. (2017). A Segmentation Model for Extracting Farmland and Woodland from Remote Sensing Image. Preprints. https://doi.org/10.20944/preprints201712.0192.v1
Chicago/Turabian Style
Zhang, C., Qing Cheng and Dejuan Song. 2017 "A Segmentation Model for Extracting Farmland and Woodland from Remote Sensing Image" Preprints. https://doi.org/10.20944/preprints201712.0192.v1
Abstract
It is very difficult to accurately divide farmland and woodland in Gaofen 2 (GF-2) remote sensing image, because their single plant coverage is very small, and their spectra are very similar. The ratio of spatial resolution and one plant’s coverage area must be fully taken into account when designing the Convolutional Neural Network structure for extracting them from GF-2 image. We establish a Convolutional Encode Neural Networks model (CENN), The first layer has two sets of convolution kernels to learn the characteristics of farmland and woodland respectively, while the second layer is the encoder to encode the characteristics by transfer function, which can map the results to the corresponding category number. In the training stage, samples of farmland, woodland, and other categories are categorically used to train CENN, as soon as training is accomplished, CENN would acquire enough ability to accurately extract farmland and woodland from remote sensing images. The final extraction result is obtained by implementing per-pixel segmentation of images used to train the CENN. CENN is compared and analyzed with others such as Deep Belief Network (DBN), Full Convolutional Network (FCN), Deeplab Model. The results of experiments show that CENN can more accurately mine the characteristics of farmland and woodland, and it achieves its goal of extracting farmland and woodland with high precision from GF-2 images.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
