Remote Sensing Image Scene Classification Using Bag of Convolutional Features
Remote Sensing Image Scene Classification Using Bag of Convolutional Features
复制标题
使用卷积特征包进行遥感图像场景分类
DOI：
10.1109/lgrs.2017.2731997
复制
发表时间：
2017-10-01
影响因子：
4.8
通讯作者：
Wei, Zhongliang
中科院分区：
文献类型：
--
作者：
Cheng, Gong;Li, Zhenpeng;Wei, Zhongliang
最近，遥感图像分类已经从像素级的解释，场景级的语义理解，其目的是标记每个场景图像与一个特定的语义类。虽然已经在开发各种遥感图像场景分类方法方面做出了重大努力，但其中大多数依赖于手工特征。在这封信中，我们提出了一种新的特征表示方法的场景分类，命名为袋卷积特征（BoCF）。与传统的基于视觉词的方法不同，视觉词通常是通过使用手工制作的特征描述符来获得的，所提出的BoCF使用现成的卷积神经网络从深度卷积特征中生成视觉词。在一个公开的遥感图像场景分类基准上进行了广泛的评估，并与现有的方法进行了比较，结果表明了所提出的BoCF方法在遥感图像场景分类中的有效性。
More recently, remote sensing image classification has been moving from pixel-level interpretation to scene-level semantic understanding, which aims to label each scene image with a specific semantic class. While significant efforts have been made in developing various methods for remote sensing image scene classification, most of them rely on handcrafted features. In this letter, we propose a novel feature representation method for scene classification, named bag of convolutional features (BoCF). Different from the traditional bag of visual words-based methods in which the visual words are usually obtained by using handcrafted feature descriptors, the proposed BoCF generates visual words from deep convolutional features using off-the-shelf convolutional neural networks. Extensive evaluations on a publicly available remote sensing image scene classification benchmark and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed BoCF method for remote sensing image scene classification.