The high intraclass variations and high interclass similarities make high spatial resolution (HSR) image interpretation very difficult. In this paper, we provide a feature selection method for scene classification, particularly to recognize the images from the industrial scene. The proposed method is based on scene overlapping rate and a quantitative feature assessment metric, considering the interclass distinguishing capabilities of features. Thus, the optimal feature representation for diverse scenes according to the discriminative characteristics of intraclass images can be selected. Specifically, an efficient scene recognition algorithm is presented to identify the easy-confused scenes from the dataset. An evaluation metric is also proposed metric to quantify the distinguishing capability of features with full statistical support. Experiments comparing 14 state-of-art classification methods on two challenging benchmark scene datasets (UCM and AID) show the effectiveness of the proposed method for HSR scene classification.
The emergence of 3D point cloud analysis has brought about new opportunities and challenges in various fields such as autonomous driving, digital twins, and virtual reality. Accurate segmentation is crucial to 3D point cloud analysis, but challenges arise due to the lack of topological information, complex shapes, and sparsity and unevenness in point sampling. To address these problems, a novel point cloud segmentation network called PCSNet (Point Cloud Segmentation Network) has been proposed. PCSNet combines global and local features to determine the overall shape and detailed local information, respectively, through an encoder-decoder architecture that incorporates multi-scale feature fusion. The encoder progressively extracts local center points, fuses local features, and models global features with the transformer to construct multi-scale topological and semantic information. The decoder then recovers the original point cloud and incorporates multi-scale features by upsampling for accurate segmentation. PCSNet outperforms state-of-the-art point cloud segmentation approaches on two widely used benchmark datasets (ShapeNetPart and S3DIS).
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.