Scene Classification via a Gradient Boosting Random Convolutional Network Framework
Scene Classification via a Gradient Boosting Random Convolutional Network Framework
复制标题
通过梯度提升随机卷积网络框架进行场景分类
DOI：
10.1109/tgrs.2015.2488681
复制
发表时间：
2016
影响因子：
8.2
通讯作者：
Du Bo
中科院分区：
文献类型：
--
作者：
Zhang Fan;Zhang Liangpei;Du Bo
由于卫星传感器的最新进展，现在每天都能获得大量高分辨率遥感图像。如何有效地从这些卫星图像中自动识别和分析场景已成为遥感领域的一大挑战。最近，人们提出了大量场景分类方面的工作，重点是深度神经网络，它从图像数据集中学习分层内部特征表示并产生最先进的性能。然而，大多数方法，包括传统的浅层方法和深层神经网络，只集中在训练单个模型。与此同时，神经网络集成已被证明是一个强大的和实用的工具，为一些不同的预测任务。我们能找到一种方法来有效和高效地将联合收割机不同的深度神经网络组合起来进行场景分类吗？在本文中，我们提出了一种用于场景分类的梯度提升随机卷积网络（GBRCN）框架，该框架可以有效地将联合收割机许多深度神经网络结合起来。据我们所知，这是第一次提出一个深度集成框架用于场景分类。此外，在实验中，所提出的方法被应用到两个具有挑战性的高分辨率数据集：1）UC默塞德数据集包含21个不同的空中场景类别与亚米级分辨率和2）悉尼数据集包含8个土地利用类别与1.0米的空间分辨率。所提出的GBRCN框架在UC默塞德数据集上的表现优于最先进的方法，包括传统的单卷积网络方法。对于悉尼数据集，所提出的方法再次获得了最佳的准确性，表明所提出的框架可以提供比最先进的方法更准确的分类结果。
Due to the recent advances in satellite sensors, a large amount of high-resolution remote sensing images is now being obtained each day. How to automatically recognize and analyze scenes from these satellite images effectively and efficiently has become a big challenge in the remote sensing field. Recently, a lot of work in scene classification has been proposed, focusing on deep neural networks, which learn hierarchical internal feature representations from image data sets and produce state-of-the-art performance. However, most methods, including the traditional shallow methods and deep neural networks, only concentrate on training a single model. Meanwhile, neural network ensembles have proved to be a powerful and practical tool for a number of different predictive tasks. Can we find a way to combine different deep neural networks effectively and efficiently for scene classification? In this paper, we propose a gradient boosting random convolutional network (GBRCN) framework for scene classification, which can effectively combine many deep neural networks. As far as we know, this is the first time that a deep ensemble framework has been proposed for scene classification. Moreover, in the experiments, the proposed method was applied to two challenging high-resolution data sets: 1) the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and 2) a Sydney data set containing eight land-use categories with a 1.0-m spatial resolution. The proposed GBRCN framework outperformed the state-of-the-art methods with the UC Merced data set, including the traditional single convolutional network approach. For the Sydney data set, the proposed method again obtained the best accuracy, demonstrating that the proposed framework can provide more accurate classification results than the state-of-the-art methods.