MyJournals Home  

RSS FeedsRemote Sensing, Vol. 14, Pages 545: Remote Sensing Scene Image Classification Based on Self-Compensating Convolution Neural Network (Remote Sensing)


24 january 2022 10:50:33

Remote Sensing, Vol. 14, Pages 545: Remote Sensing Scene Image Classification Based on Self-Compensating Convolution Neural Network (Remote Sensing)

In recent years, convolution neural networks (CNNs) have been widely used in the field of remote sensing scene image classification. However, CNN models with good classification performance tend to have high complexity, and CNN models with low complexity are difficult to obtain high classification accuracy. These models hardly achieve a good trade-off between classification accuracy and model complexity. To solve this problem, we made the following three improvements and proposed a lightweight modular network model. First, we proposed a lightweight self-compensated convolution (SCC). Although traditional convolution can effectively extract features from the input feature map, when there are a large number of filters (such as 512 or 1024 common filters), this process takes a long time. To speed up the network without increasing the computational load, we proposed a self-compensated convolution. The core idea of this convolution is to perform traditional convolution by reducing the number of filters, and then compensate the convoluted channels by input features. It incorporates shallow features into the deep and complex features, which helps to improve the speed and classification accuracy of the model. In addition, we proposed a self-compensating bottleneck module (SCBM) based on the self-compensating convolution. The wider channel shortcut in this module facilitates more shallow information to be transferred to the deeper layer and improves the feature extraction ability of the model. Finally, we used the proposed self-compensation bottleneck module to construct a lightweight and modular self-compensation convolution neural network (SCCNN) for remote sensing scene image classification. The network is built by reusing bottleneck modules with the same structure. A lot of experiments were carried out on six open and challenging remote sensing image scene datasets. The experimental results show that the classification performance of the proposed method is superior to some of the state-of-the-art classification methods with less parameters.

73 viewsCategory: Geology, Physics
Remote Sensing, Vol. 14, Pages 544: Determination of Weak Terrestrial Water Storage Changes from GRACE in the Interior of the Tibetan Plateau (Remote Sensing)
Remote Sensing, Vol. 14, Pages 546: Spatio-Temporal Estimation of Rice Height Using Time Series Sentinel-1 Images (Remote Sensing)
blog comments powered by Disqus
The latest issues of all your favorite science journals on one page


Register | Retrieve



Copyright © 2008 - 2022 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten