Abstract:

Greenhouse is of great significance to agricultural production. Due to the influence of seasons and environment, the features of remote sensing images in different periods are different, and the accuracy of single-temporal features can not meet the requirements. In recent years, deep learning has been proved to be suitable for the classification of remote sensing data. In order to realize the effective application of deep learning in agricultural remote sensing, this paper designs an improved multi-temporal semantic segmentation model called Multi-temporal Spatial Segmentation Network（MSSN） for greenhouse extraction. The Patch-LSTM（based on Patch long time-memory network） is proposed, which makes full use of the spatial and temporal information of images. Spatial pyramid pooling with void convolution （ASSP） is used to solve the problem of network sensitivity to scale. The skip-layer and deconvolution layer are further added to improve the reduction ability of the feature map Shandong Gaomi is chosen as the experimental area, and the multi temporal GF-2 remote sensing image is used for the experiment. The results show that the segmentation model has 0.95 precision, 0.92 F1 score and 0.93 foreground IOU（Intersection Over Union） on the test set. The segmentation model can achieve high-precision greenhouse extraction and provide a new method for the application of deep learning in agricultural remote sensing.

Key words: agricultural remote sensing, greenhouses, multi-temporal, Patch-LSTM, semantic segmentation model, Convolutional Neural Network（CNN）

摘要：

蔬菜大棚对于农业生产具有重要意义。受季节和环境影响，其在遥感影像上不同时期呈现不同形态，仅通过单时相特征提取精度不能满足要求。近几年，深度学习被证明适合遥感数据的分类，为实现深度学习在农业遥感上的有效应用，提出了一种改进的多时相语义分割模型（Multi-temporal Spatial Segmentation Network，MSSN）用于蔬菜大棚提取。提出基于补丁长短时记忆网络（Patch-LSTM），该网络充分利用图像的空间和时序信息。采用带空洞卷积的空间金字塔池化（ASSP）解决网络对尺度敏感问题。进一步添加跳连层（Skip-layer）和反卷积层提升特征图的还原能力。选择山东高密GF-2遥感影像进行实验。结果表明，该分割模型在测试集上有0.95的Precision、0.92的F1 score以及0.93的前景IoU（Intersection Over Union），可以实现高精度的蔬菜大棚提取，为深度学习在农业遥感的应用提供新的方法。

关键词: 农业遥感, 蔬菜大棚, 多时相, 基于补丁长短时记忆网络, 语义分割模型, 卷积神经网络