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Convolutional neural networks (CNNs) has been introduced into remote sensing scene classification, achieving outstanding performance. However, the scale change of objects contained in remote sensing scene image make it difficult to extract feature robust to scale, limiting the further improvement of classification accuracy. In this paper, a scene classification method named Scale Invariance Convolutional...
Convolutional neural network (CNN) has outstanding performance on nature image classification, such as facial recognition, ImageNet Large Scale Visual Recognition Challenge. However, due to scale variation of the same object in scene, it's difficult to directly utilize CNN for remote sensing image classification. In order to solve this problem, scene classification based on a random-scale stretched...
This paper proposes a novel local contrast pattern (LCP) to drive the histogram-based Chan-Vese (CV) model for texture image segmentation. The local contrast pattern has two maps, differential contrast map and orientation map, which are well suited to describe texture structure, especially the texture orientation information. In order to enable the extraction of accurate local texture features, a...
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