Land cover mapping and monitoring are essential for understanding the environment and the effects of human activities on it. The automatic approaches to land cover mapping are predominantly based on the traditional machine learning that requires heuristic feature design. Such approaches are relatively slow and often suitable only for a particular type of satellite sensor or geographical area. Recently, deep learning has outperformed traditional machine learning approaches on a range of image processing tasks including image classification and segmentation. In this study, we demonstrated the suitability of deep learning models for wide-area land cover mapping using satellite C-band SAR images. We used a set of 14 ESA Sentinel-1 scenes acquired during the summer season in Finland representative of the land cover in the country. These imageries were used as an input to seven state-of-the-art deep-learning models for semantic segmentation, namely U-Net, DeepLabV3+, PSPNet, BiSeNet, SegNet, FC-DenseNet, and FRRN-B. These models were pre-trained on the ImageNet dataset and further fine-tuned in this study. CORINE land cover map produced by the Finnish Environment Institute was used as a reference, and the models were trained to distinguish between 5 Level-1 CORINE classes. Upon the evaluation and benchmarking, we found that all the models demonstrated solid performance, with the top FC-DenseNet model achieving an overall accuracy of 90.7%. These results indicate the suitability of deep learning methods to support efficient wide-area mapping using satellite SAR imagery.
翻译:土地覆盖测绘和监测对于了解环境和人类活动对土地覆盖的影响至关重要。土地覆盖测绘的自动方法主要基于传统机器学习,需要超自然特征设计。这些方法相对缓慢,往往只适用于特定类型的卫星传感器或地理区域。最近,深学习在一系列图像处理任务(包括图像分类和分割)方面优于传统机器学习方法。在这项研究中,我们展示了利用卫星C波段合成合成孔径雷达图像进行广域土地覆盖测绘的深学习模型的适宜性。我们使用了芬兰夏季获得的一套14个欧空局哨兵-1场景,这些场景代表了芬兰土地覆盖的夏季。这些图像被用于为七种最先进的卫星传感器或地理区段提供素材,即U-Net、DeepLabV3+、PSPNet、BisNet、SeNet、SegNet、FC-DenseNet和FRRN-B。这些模型在图像网络成像网数据集中进行了预先培训,并在这项研究中作了进一步的调整。COR网络地面覆盖地图,使用芬兰环境研究所制作的精度精确度地图作为投入,我们所培训的SAL5级模型的成绩评估。这些模型与SALA级的高级模型进行了对比。我们进行了实地评估,在SLA级的成绩评估,在SALA级模型上进行了实地评估,并进行了实地评估,在SLA级模型上进行了实地评估,在S-S-S-SALA级中进行了测试。