Abstract
The scene classification of a remote sensing image has been widely used in various fields as an important task of understanding the content of a remote sensing image. Specially, a high-resolution remote sensing scene contains rich information and complex content. Considering that the scene content in a remote sensing image is very tight to the spatial relationship characteristics, how to design an effective feature extraction network directly decides the quality of classification by fully mining the spatial information in a high-resolution remote sensing image. In recent years, convolutional neural networks (CNNs) have achieved excellent performance in remote sensing image classification, especially the residual dense network (RDN) as one of the representative networks of CNN, which shows a stronger feature learning ability as it fully utilizes all the convolutional layer information. Therefore, we design an RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image. First, multi-layer convolutional features are fused with residual dense blocks. Then, a channel-spatial attention module is added to obtain more effective feature representation. Finally, softmax classifier is applied to classify the scene after adopting data augmentation strategy for meeting the training requirements of the network parameters. Five experiments are conducted on the UC Merced Land-Use Dataset (UCM) and Aerial Image Dataset (AID), and the competitive results demonstrate that our method can extract more effective features and is more conducive to classifying a scene.
Highlights
High-resolution remote sensing images are the basic data of spatial information technologies in geographic information systems, global navigation satellite systems and important basic and strategic information resources of the country [1,2,3,4,5,6]
In order to solve the above problems, we propose an residual dense network (RDN)-based on channel-spatial attention for the scene classification of a high-resolution remote sensing image
We apply the UC Merced Land-Use Dataset (UCM) and Aerial Image Dataset (AID) that are commonly used on remote sensing scene classification to demonstrate the performance of the pRermoopteoSseends. m202e0t,h1o2d, 1.8T87he characteristics of these two datasets are described in detail below
Summary
High-resolution remote sensing images are the basic data of spatial information technologies in geographic information systems, global navigation satellite systems and important basic and strategic information resources of the country [1,2,3,4,5,6]. It has become a research hotspot that analyzing and processing the content of high-resolution remote sensing images in recent years [12,13,14]. The scene classification of a high-resolution remote sensing image refers to distinguishing specific areas present in the image, such as ocean, land, vegetation, etc., and is widely applied in urban planning, geographic image retrieval and other fields [15,16,17]. How to design an effective feature extraction network to fully mine the spatial information of a high-resolution remote sensing image directly determines the quality of classification
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.