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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Scene Classification via a Gradient Boosting Random Convolutional Network Framework
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Scene Classification via a Gradient Boosting Random Convolutional Network Framework

机译：通过梯度提升随机卷积网络框架进行场景分类

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摘要

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.
机译：由于卫星传感器的最新发展，现在每天都在获取大量的高分辨率遥感图像。如何有效，高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。近来，已提出了许多场景分类方面的工作，重点是深层神经网络，该网络从图像数据集中学习分层的内部特征表示并产生最新的性能。但是，大多数方法，包括传统的浅层方法和深层神经网络，都只专注于训练单个模型。同时，神经网络集成已被证明是用于许多不同预测任务的强大而实用的工具。我们是否可以找到一种方法，将不同的深度神经网络有效地结合起来，以进行场景分类？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，该框架可以有效地结合许多深度神经网络。据我们所知，这是首次提出用于场景分类的深度集成框架。此外，在实验中，将所提出的方法应用于两个具有挑战性的高分辨率数据集：1）UC Merced数据集包含21个亚米级分辨率的空中场景类别，以及2）Sydney数据集包含八个土地用途类别具有1.0米的空间分辨率。提出的GBRCN框架在包括单个传统卷积网络方法在内的UC Merced数据集方面胜过了最新技术。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新方法更准确的分类结果。

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