Oracle bone inscriptions (OBIs) are invaluable materials for recovering the economic and social forms for Shang Dynasty, one of the most ancient dynasties in China. It is very important to get the original OBIs from scanned images of oracle bone rubbings. To this end, researchers have to employ a very time-consuming method that they follow the inscriptions by handwritten tools, pixel by pixel and image by image. In this paper, an image segmentation method was proposed to overcome this limitation based on fully convolutional networks (FCN). In order to speed up training as well as boost the segmentation performance, a simple FCN with only convolutional layers was designed, where batch normalization was incorporated. The proposed method was tested on a real OBI image set (320 samples). Experimental results show that the proposed method is effective enough to get the OBIs from scanned images of oracle bone rubbings.
It is very important for the government to build an accurate national basic cultivated land database. In this work, farmland parcels extraction is one of the basic steps. However, during the past years, people had to spend much time on determining an area is a farmland parcel or not, since they were bounded to understand remote sensing images only from the mere visual interpretation. In order to overcome this problem, in this study, a method was proposed to extract farmland parcels by means of image classification. In the proposed method, farmland areas and ridge areas of the classification map are semantically processed independently and the results are fused together to form the final results of farmland parcels. Experiments on high spatial remote sensing images have shown the effectiveness of the proposed method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.