Abstract
Convolutional neural network (CNN) is capable of automatically extracting image features and has been widely used in remote sensing image classifications. Feature extraction is an important and difficult problem in current research. In this paper, data augmentation for avoiding over fitting was attempted to enrich features of samples to improve the performance of a newly proposed convolutional neural network with UC-Merced and RSI-CB datasets for remotely sensed scene classifications. A multiple grouped convolutional neural network (MGCNN) for self-learning that is capable of promoting the efficiency of CNN was proposed, and the method of grouping multiple convolutional layers capable of being applied elsewhere as a plug-in model was developed. Meanwhile, a hyper-parameter C in MGCNN is introduced to probe into the influence of different grouping strategies for feature extraction. Experiments on the two selected datasets, the RSI-CB dataset and UC-Merced dataset, were carried out to verify the effectiveness of this newly proposed convolutional neural network, the accuracy obtained by MGCNN was 2% higher than the ResNet-50. An algorithm of attention mechanism was thus adopted and incorporated into grouping processes and a multiple grouped attention convolutional neural network (MGCNN-A) was therefore constructed to enhance the generalization capability of MGCNN. The additional experiments indicate that the incorporation of the attention mechanism to MGCNN slightly improved the accuracy of scene classification, but the robustness of the proposed network was enhanced considerably in remote sensing image classifications.
Highlights
With the rapid advance of remote sensing and earth observation technology, high spatial resolution [1,2] (HSR) remote sensing (RS) imagery with sub-meter level spatial resolution or even very high resolution (VHR) RS imagery [3,4] with centimeter-level resolution became widely available and accessible to the public
Two grouped convolutional neural networks aimed for remotely sensed image scene classifications, namely, multiple grouped convolutional neural network (MGCNN) and multiple grouped attention convolutional neural network (MGCNN-A) developed on the basis of ResNet-50, were proposed and tested with RSI-CB and UC-Merced datasets
Data augmentation scheme was experimentally applied to three popularized convolutional neural networks, i.e., VGGNet-16, GoogLeNet-22, and ResNet-50, to investigate their performances in remotely sensed image scene classifications; the results strongly suggested the effectiveness of data augmentation in improving performance of classifications with these networks, and the ResNet-50 performed the best according to several criterions
Summary
With the rapid advance of remote sensing and earth observation technology, high spatial resolution [1,2] (HSR) remote sensing (RS) imagery with sub-meter level spatial resolution or even very high resolution (VHR) RS imagery [3,4] with centimeter-level resolution became widely available and accessible to the public. Combined with clustering methods like K-Means, these features are mapped into a visual dictionary and generate a feature histogram for each image with bag of visual word (BOVW) [17]. This method relies heavily on handcrafted features, and the clustering method requires expert experience and knowledge. GoogLeNet [20] used convolution kernels of different sizes to construct the inception structure, it can extract multi-scale features and used the global pooling layer to replace the full connection layer, which reduced the amount of computation and improved the performance of the network. The following networks all adopted the advantages of the previous networks and got improved based on them: DenseNet [22]
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