Based on YOLOX network, this paper presented an algorithm for extracting point-like independent houses from remote sensing images. First, an Adaptively Spatial Feature Fusion (ASFF) network was added to the feature extraction module PANet to deeply mine the detailed features of small target houses with different scales. Second, a feature extraction module based on ECA local cross-channel interaction attention mechanism was designed. Efficient channel interaction paid more attention to the positive sample feature information in the feature map and lowered the complicacy of the model. Finally, the Swish activation function was used to avert poor activation effect. Experiments were conducted on the point-like independent houses data set, and the optimum mechanism and effectiveness of the improved method were validated by qualitative analysis of ablation experiments and quantitative analysis of comparison experiments. On the premise of adding ASFF mechanism and ECA attention mechanism and optimizing Swish activation function, the mAP precision of the improved network model was up to 94.83%, 11.16% higher than that of the original network. The robustness and effectiveness of the improved method were quantitatively verified by conducting comparative experiments with widely used detection algorithms.
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.