Guo Zhiling, Chen Qi, Wu Guangming, Xu Yongwei, Shibasaki Ryosuke, Shao Xiaowei
Center for Spatial Information Science, University of Tokyo, Kashiwa 277-8568, Japan.
Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China.
Sensors (Basel). 2017 Oct 30;17(11):2487. doi: 10.3390/s17112487.
In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86.
在本研究中，我们提出了集成卷积神经网络（ECNN），这是一种基于集成最先进的卷积神经网络模型构建的精细卷积神经网络框架，用于从公开的高分辨率遥感（HRRS）图像中识别乡村建筑。首先，为了优化和挖掘卷积神经网络在乡村地图绘制方面的能力，并确保与我们的分类目标兼容，基于一系列严格的分析和评估，对一些最先进的模型进行了精心优化和改进。其次，我们并非直接使用这些模型来进行建筑识别，而是基于多尺度特征学习方法，将它们的特征提取部分集成到一个名为ECNN的更强模型中，从而充分利用它们的大部分优势。最后，将生成的ECNN应用于像素级分类框架以实现目标识别。所提出的方法可以作为一种可行的工具，以高精度和高效率进行乡村建筑识别。从老挝沙湾拿吉省试验区获得的实验结果证明，所提出的ECNN模型显著优于现有方法，总体准确率从96.64%提高到99.26%，卡帕系数从0.57提高到0.86。
