How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

Xin Zhang,Liangxiu Han,Liang Zhu,Lianghao Han

doi:10.3390/rs12030417

Abstract

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.

Highlights

The term “land cover” refers to the man-made and natural characteristics of the Earth’s surface, such as water, soil, natural vegetation, crops, and human infrastructure [1]
The two methods have similar performances in terms of F score, U-Net performs slightly better than SegNet
We have reviewed the current, commonly used land cover mapping methods, including land cover classification and object detection methods based on high resolution images

Summary

Introduction

The term “land cover” refers to the man-made and natural characteristics of the Earth’s surface, such as water, soil, natural vegetation, crops, and human infrastructure [1]. The land cover and its changes at both regional and global levels can affect our health, environment, etc. Some have been identified as fundamental variables for describing and studying Earth’s ecosystems, such as food production, land management and planning, disaster monitoring, climate change, and carbon circulation [2,3]. Remote sensing data from satellites, aircraft, or Unmanned aerial vehicles (UAVs) have been widely used for land cover to map and monitor the changes of the Earth’s diverse landscapes from Space. A variety of land cover classification and objective detection methods on remote sensed data with high spatial resolutions have been proposed. They can be classified into two categories: pixel-based and object-based approaches

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Discussion

Conclusion