How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?
How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?
复制标题
基于深度学习的土地覆盖分类和目标检测方法在高分辨率遥感影像上的表现如何？
DOI：
10.3390/rs12030417
复制
发表时间：
2020-02-01
期刊：
影响因子：
5
通讯作者：
Zhu, Liang
中科院分区：
文献类型：
--
作者：
Zhang, Xin;Han, Liangxiu;Zhu, Liang
土地覆盖信息在绘制地球不同地貌的生态和环境变化图以进行生态系统监测方面发挥着重要作用。遥感数据已被广泛用于研究土地覆盖，从而能够有效地从太空绘制地球表面变化的地图。尽管高分辨率遥感影像的可用性每年都在显著增加，但传统的基于像元和目标水平的土地覆盖分析方法并不是最优的。深度学习的最新进展在图像识别领域取得了显著的成功，并在高空间分辨率的遥感应用中显示出了潜力，包括分类和目标检测。本文对高分辨率遥感影像的土地覆盖分类和目标检测方法进行了综述。通过两个案例，我们展示了最新的深度学习模型在高空间分辨率遥感数据土地覆盖分类和目标检测中的应用，并与传统方法进行了性能比较。对于土地覆盖分类任务，基于深度学习的方法通过使用空间信息和光谱信息提供了端到端的解决方案。与传统的基于像素的方法相比，它们表现出了更好的性能，特别是对于不同类别的植被。对于目标检测任务，基于深度学习的目标检测方法在大范围内达到了98%以上的准确率，其高精度和高效率可以减轻传统的、劳动强度大的方法的负担。然而，考虑到遥感数据的多样性，为了提高基于深度学习的模型的泛化能力和鲁棒性，需要更多的训练数据集。
Land cover information plays an important role in mapping ecological and environmental changes in Earth's diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown potential in high spatial resolution remote sensing applications, including classification and object detection. In this paper, a comprehensive review on land cover classification and object detection approaches using high resolution imagery is provided. Through two case studies, we demonstrated the applications of the state-of-the-art deep learning models to high spatial resolution remote sensing data for land cover classification and object detection and evaluated their performances against traditional approaches. For a land cover classification task, the deep-learning-based methods provide an end-to-end solution by using both spatial and spectral information. They have shown better performance than the traditional pixel-based method, especially for the categories of different vegetation. For an objective detection task, the deep-learning-based object detection method achieved more than 98% accuracy in a large area; its high accuracy and efficiency could relieve the burden of the traditional, labour-intensive method. However, considering the diversity of remote sensing data, more training datasets are required in order to improve the generalisation and the robustness of deep learning-based models.