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首页> 外文期刊>International journal of applied mechanics >How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?
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How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

机译：基于深度学习的土地覆盖分类和对象检测的方法如何在高分辨率遥感图像上进行何处？

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摘要

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.
机译：土地覆盖信息在绘制地球各种景观中的生态和环境变化中起着重要作用，以获得生态系统监测。遥感数据已广泛用于陆地覆盖的研究，从而有效地绘制了地球表面的变化。虽然每年的高分辨率遥感图像的可用性显着增加，但基于像素和物体级别的传统土地覆盖分析方法不是最佳的。深度学习的最新进步在图像识别领域取得了显着的成功，并在高空间分辨率遥感应用中显示了潜力，包括分类和对象检测。在本文中，提供了使用高分辨率图像的土地覆盖分类和物体检测方法进行全面审查。通过两种案例研究，我们证明了最先进的深度学习模型应用于高空间分辨率的遥感数据，用于土地覆盖分类和对象检测，并评估其针对传统方法的性能。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和光谱信息提供端到端解决方案。它们表现出比传统的基于像素的方法更好的性能，特别是对于不同植被的类别。对于客观检测任务，基于深度学习的物体检测方法在大面积中实现了超过98％的精度;其高精度和效率可以缓解传统，劳动密集型方法的负担。然而，考虑到遥感数据的多样性，需要更多的训练数据集来提高基于深度学习模型的泛化和鲁棒性。

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