Abstract
High spatial resolution remote sensing (HSRRS) images contain complex geometrical structures and spatial patterns, and thus HSRRS scene classification has become a significant challenge in the remote sensing community. In recent years, convolutional neural network (CNN)-based methods have attracted tremendous attention and obtained excellent performance in scene classification. However, traditional CNN-based methods focus on processing original red-green-blue (RGB) image-based features or CNN-based single-layer features to achieve the scene representation, and ignore that texture images or each layer of CNNs contain discriminating information. To address the above-mentioned drawbacks, a CaffeNet-based method termed CTFCNN is proposed to effectively explore the discriminating ability of a pre-trained CNN in this paper. At first, the pretrained CNN model is employed as a feature extractor to obtain convolutional features from multiple layers, fully connected (FC) features, and local binary pattern (LBP)-based FC features. Then, a new improved bag-of-view-word (iBoVW) coding method is developed to represent the discriminating information from each convolutional layer. Finally, weighted concatenation is employed to combine different features for classification. Experiments on the UC-Merced dataset and Aerial Image Dataset (AID) demonstrate that the proposed CTFCNN method performs significantly better than some state-of-the-art methods, and the overall accuracy can reach 98.44% and 94.91%, respectively. This indicates that the proposed framework can provide a discriminating description for HSRRS images.
Highlights
With the improvement of Earth observation technology, great progress has been made in the collection of high spatial resolution remote sensing (HSRRS) images [1,2,3]
In [22], three kinds of pre-trained convolutional neural network (CNN) models were employed as feature extractors, and the features from the first fully connected layer were used for classification
We proposed a combines triple-part features of convolutional neural networks (CTFCNN) framework to fully exploit the discriminant ability of a pre-trained CaffeNet
Summary
With the improvement of Earth observation technology, great progress has been made in the collection of high spatial resolution remote sensing (HSRRS) images [1,2,3]. Compared with low spatial resolution remote sensing images, an HSRRS image contains more details of ground objects and more complex spatial patterns [4,5,6,7,8]. HSRRS-image-based scene classification has attracted increasing attention in the remote sensing community [15,16,17,18,19,20]. Traditional scene classification methods were developed directly based on low-level features, such as texture features, color features, spectral features, and multi-feature fusion [23,24]. These hand-crafted features are limited in describing complex scenes of HSRRS, which will affect the classification performance
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