戴佩玉, 李世忠, 季顺平, 任妮. 一种基于域自适应泛化增强的云检测方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220435
引用本文: 戴佩玉, 李世忠, 季顺平, 任妮. 一种基于域自适应泛化增强的云检测方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220435
Dai Peiyu, Li Shizhong, Ji Shunping, Ren Ni. A cloud detection method with domain adaptation enhanced generalization capability[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220435
Citation: Dai Peiyu, Li Shizhong, Ji Shunping, Ren Ni. A cloud detection method with domain adaptation enhanced generalization capability[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220435

一种基于域自适应泛化增强的云检测方法

A cloud detection method with domain adaptation enhanced generalization capability

  • 摘要: 由于遥感传感器光谱范围、成像条件、成像时间差异,不同传感器获得的遥感影像之间普遍存在着色彩和光谱差异。这导致利用开源数据集预训练的云检测模型往往难以直接应用于当前目标影像的云检测任务中。本文提出了一种基于域自适应的云检测算法,实现目标数据集遥感影像与现有训练数据集影像之间的光谱映射,使得不同传感器、不同地理区域的目标数据集与训练数据集尽可能相似,以提高深度学习模型的泛化能力以及预训练云检测模型在实际应用中的鲁棒性。具体地,本文的基于域适应的云检测框架以一种基于卷积神经元网络的云检测模型作为预测算法,以CycleGAN作为源域和目标域的影像之间的光谱迁移算法。在全球范围内分布的高分2、Landsat7,Landsat8等数据间的跨域实验,证明了本文提出方法的有效性和先进性。

     

    Abstract: Objectives: Due to the differences in spectral range, imaging conditions and imaging time of remote sensing sensors, there are usually color and spectral differences between remote sensing images obtained by different sensors. Therefore, it is difficult to directly apply the model pretrained with open-source datasets for the cloud detection task on current target images. Methods: In this paper, a domain adaptive cloud detection algorithm is proposed, which realizes the spectral mapping between the target remote sensing images and the images of existing training data sets, and improves the generalization ability and robustness of the pretrained deep learning cloud detection model in practical applications. Specifically, the domain adaptation based cloud detection framework in this paper uses a convolutional neural network based cloud detection model as the prediction algorithm, and CycleGAN as the spectral domain transfer between images in the source domain and the target domain. Results: The cross-domain experiments among GaoFen2 (GF2), Landsat7, landsat8 have proved the effectiveness and advance of the proposed method. Conclusions can be obtained on three different datasets: 1) When the test dataset and training dataset are obtained from the same sensor, deep learning-based cloud detection algorithms obtain better results than the traditional cloud detection algorithms. 2) When cloud detection models trained on images from the source dataset are directly used to detect test images of the target dataset, the accuracy decreases obviously. 3) When the test images are pre-transformed by the introduced domain transfer algorithm, the accuracy of the cloud detection results is significantly improved and much better than the traditional methods. In addition, we discovered that when the spectral range of source training images covers the spectral range of test images, the accuracy of the cloud detection results decreases only slightly, but in the opposite case, the accuracy of the cloud detection results decreases significantly. Conclusions: Domain transfer can map images between the test dataset and the training dataset at the pixel level, so that the distributions of them are similar both in spectral and spatial space. By introducing domain adaptation, the deep learning-based cloud detection method can make full use of the pre-trained models from existing datasets, which greatly reduces the demand on preparation of target labels.

     

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