Li Linyi, Xu Tingbao, Chen Yun
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia.
Comput Intell Neurosci. 2017;2017:9858531. doi: 10.1155/2017/9858531. Epub 2017 Jul 6.
In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.
近年来，遥感图像的空间分辨率有了很大提高。然而，较高空间分辨率的图像并不总是能带来更好的自动场景分类结果。视觉注意力是人类视觉系统的一个重要特征，它可以有效地帮助对遥感场景进行分类。在本研究中，提出了一种新颖的视觉注意力特征提取算法，该算法通过多尺度过程提取视觉注意力特征。并且开发了一种使用视觉注意力特征的模糊分类方法（FC-VAF）来进行高分辨率遥感场景分类。通过使用来自广泛使用的高分辨率遥感图像（包括IKONOS、QuickBird和ZY-3图像）的遥感场景对FC-VAF进行了评估。根据定量精度评估指标，FC-VAF比其他方法取得了更准确的分类结果。我们还讨论了不同分解级别和不同小波对分类精度的作用和影响。FC-VAF提高了高分辨率场景分类的精度，从而推动了数字图像分析研究以及高分辨率遥感图像的应用。
