Abstract
Deep convolutional neural networks (DCNNs) have shown significant improvements in remote sensing image scene classification for powerful feature representations. However, because of the high variance and volume limitations of the available remote sensing datasets, DCNNs are prone to overfit the data used for their training. To address this problem, this paper proposes a novel scene classification framework based on a deep Siamese convolutional network with rotation invariance regularization. Specifically, we design a data augmentation strategy for the Siamese model to learn a rotation invariance DCNN model that is achieved by directly enforcing the labels of the training samples before and after rotating to be mapped close to each other. In addition to the cross-entropy cost function for the traditional CNN models, we impose a rotation invariance regularization constraint on the objective function of our proposed model. The experimental results obtained using three publicly-available scene classification datasets show that the proposed method can generally improve the classification performance by 2~3% and achieves satisfactory classification performance compared with some state-of-the-art methods.
Highlights
Remote sensing image scene classification [1] has been widely used in many practical applications such as urban planning, environment monitoring, and natural hazard detection [2,3,4,5,6]
Many deep convolutional neural network (DCNN)-based models have been proposed [13,14,15,16], replacing scene classification based on handcrafted-features such as color histograms [17], scale-invariant feature transform (SIFT) [18], histogram of oriented gradients (HOG) [19], and global image descriptor (GIST) [20]
These models can achieve better classification performance due to the powerful feature representation and generalization ability of pre-trained DCNN models (e.g., AlexNet [21], VGG-VD16 [22], GoogLeNet [23], and ResNet [24]) on the ImageNet [25]. These methods have significantly improved the classification performance, two challenging problems still remain in remote sensing scene classification: the small scale of labeled training data has limited the potential of DCNN-based methods for scene classification due to the overfitting problem; the orientations of geospatial objects are diverse because remote sensing images are taken from the upper airspace, but objects in the nature scene images generally show small orientation variations due to the gravity of Earth [26]
Summary
Remote sensing image scene classification [1] has been widely used in many practical applications such as urban planning, environment monitoring, and natural hazard detection [2,3,4,5,6]. Many deep convolutional neural network (DCNN)-based models have been proposed [13,14,15,16], replacing scene classification based on handcrafted-features such as color histograms [17], scale-invariant feature transform (SIFT) [18], histogram of oriented gradients (HOG) [19], and global image descriptor (GIST) [20] These models can achieve better classification performance due to the powerful feature representation and generalization ability of pre-trained DCNN models (e.g., AlexNet [21], VGG-VD16 [22], GoogLeNet [23], and ResNet [24]) on the ImageNet [25]. These methods have significantly improved the classification performance, two challenging problems still remain in remote sensing scene classification: the small scale of labeled training data has limited the potential of DCNN-based methods for scene classification due to the overfitting problem; the orientations of geospatial objects are diverse because remote sensing images are taken from the upper airspace, but objects in the nature scene images generally show small orientation variations due to the gravity of Earth [26]
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