School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China.
Sensors (Basel). 2018 Sep 25;18(10):3232. doi: 10.3390/s18103232.
Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union () and outperforms other state-of-the-art methods in remote sensing image segmentation.
高效准确的语义分割是自动遥感图像分析的关键技术。虽然已经有许多基于传统手工特征提取器的分割方法，但处理高分辨率和大规模遥感图像仍然具有挑战性。在这项工作中，提出了一种新的基于全卷积网络的新型基于补丁的语义分割方法，用于分割常见的土地资源。首先，为了处理高分辨率图像，将图像分割为局部补丁，然后构建一个基于补丁的网络。其次，通过多种方式预处理训练数据，以满足遥感图像的特定特征，即颜色不平衡、目标旋转变化和镜头失真。第三，开发了一种多尺度训练策略来解决严重的尺度变化问题。此外，还研究了条件随机场（CRF）的影响，以提高精度。该方法在一个来自中国西部一个首府城市的高分辨率 2 号卫星数据集上进行了评估。该数据集包含十种常见的土地资源（草原、道路等）。实验结果表明，所提出的算法在平均交并比（IoU）方面达到 54.96%，优于遥感图像分割的其他最新方法。
