Building segmentation from high spatial resolution remote sensing images is still a great challenge in the field of remote sensing image processing. This paper proposes an end-to-end deep convolutional neural network method for building semantic segmentation using high resolution remote sensing images. In this method, a multi-scale edge enhancement module(MEEM) with the Laplacian operator as the core is embedded to extract the building edge, which is used as an auxiliary method of semantic information to improve the segmentation accuracy. Based on the experimental results of the WHU Aerial imagery dataset, it is shown that the proposed method can not only achieve the same performance as some of the best building segmentation methods but also solve the problem that small buildings are difficult to be correctly detected.
This paper proposes a shadow extraction method for GF-2 remote sensing images based on Stacking. The method uses Multiple Linear Regression Model as the second layer model to fuse the results of shadow extraction obtained by NDUI, NIS and C3 which are used as the first-layer base learners of the model. The comparison experiments demonstrate that the proposed method is superior to these traditional shadow operators such as NDUI, NSI and C3 in the accuracy of building shadow extraction. Finally, the height of the buildings is estimated using the shadow lengths and the imaging geometry method. The experimental results show that the average error of the building's height estimated is less than 1m.
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.