珠海一号高光谱影像云检测算法

珠海一号高光谱卫星具有高空间、高光谱、高时间分辨率等特点，有效推动了高光谱遥感数据在农林环境、自然资源探测等领域的广泛应用，其中高精准的云检测是遥感数据预处理的关键步骤。如何对高光谱图像有效特征提取并克服传统云检测方法特征复杂、算法参数多、计算量大、鲁棒性差等缺陷，是高光谱云检测研究的关键问题。为此，提出了一种多尺度特征融合的U型结构网络，模型首先利用残差模块进行特征编码，并将编码进行多尺度融合，在网络的跳跃连接处引入了坐标注意力机制提取有用信息，最后通过残差解码得到输出结果。实验前首先利用主成分分析降维，将高光谱数据重构为4维影像数据，然后通过数据标注与数据增强，建立珠海一号高光谱影像云检测数据集。采用了38-Cloud云数据集训练初始网络参数，随后利用构建的数据集进行迁移学习。实验结果表明，对于所建立的珠海一号高光谱云检测数据集，所提方法的像素准确率达到92.28%，可以实现高精度的高光谱遥感影像云检测。

Cloud detection algorithm based on Zhuhai-1 hyperspectral image

Zhuhai-1 hyperspectral satellite has the characteristics of high spatial, high spectral and high temporal resolution, which effectively promotes the wide application of hyperspectral remote sensing data in the fields of agriculture, forestry and natural resources detection, among which high precision cloud detection is the key step of remote sensing data preprocessing. How to effectively extract features from hyperspectral images and overcome the defects of traditional cloud detection methods, such as complex features, many algorithm parameters, large amount of computation, and poor robustness, is a key issue in the research of hyperspectral cloud detection. In this paper, a U-shaped structure network with multi-scale feature fusion is proposed. The model firstly uses the residual module for feature coding and multi-scale fusion of coding. The coordinate attention mechanism is introduced at the jump junction of the network to extract useful information, and finally the output is obtained by residual decoding. Before the experiment, principal component analysis (PCA) was used to reduce the dimensionality of hyperspectral data to reconstruct the 4D image data. Then, through data annotation and data enhancement, the Zhuhai-1 hyperspectral image cloud detection dataset was established. In this paper, 38-Cloud Cloud data is used to train the initial network parameters, and then the constructed data sets are used for transfer learning. The experimental results show that for the established Zhuhai-1 satellite hyperspectral cloud detection dataset, the pixel accuracy of the proposed method reaches 92.28%, which can realize high precision hyperspectral remote sensing image cloud detection.