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收稿：2019-03-20，
修回：2019-5-10，
录用：2019-5-17，
纸质出版：2019-11-16
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目的
2
针对农作物种植趋向集中化、机械化和庄园化的现状，高分辨率遥感影像精准识别技术已广泛应用于农作物分类。研究表明，采用先进的深度学习算法挖掘高分辨率农作物影像信息，有利于高效地分析农作物长势和参量预测，为此，提出一种改进型深度神经网络（DeepLab）的高分辨率果园遥感图像分割算法。
方法
2
首先提取原始数据的极化特征和基于相干/非相干分解的特征组成高维特征空间，然后选用流行学习降维方式获得最优3通道特征向量构成伪彩图，利用深度可分离网络（xception）、空洞卷积网络（atrous convolution）、多孔空间金字塔（ASPP）和上采样（upsample）搭建DeepLab的编码解码过程（encoder-decoder），最后将伪彩训练集和标签导入搭建的DeepLab进行训练并保存模型，利用模型对目标数据进行有效分类。
结果
2
利用本算法对中国海南某地的Ⅰ期芒果、Ⅱ期芒果、Ⅲ期芒果、槟榔、龙眼5类水果进行分类，针对不同时期的同一种水果分类错误率下降了8%左右，相比传统的果园分类算法，本算法的kappa系数提高了约0.1，总体分类精度（OA）也有一定程度的提高。
结论
2
本算法在保证不同类别水果分类准确率的基础上，提高了不同时期的同一类水果的分类准确率，在一定程度上提高了农作物长势分析的准确性，保证了高分辨率果园数据分析的可靠性。
Objective
2
With the growing national economy and the increasing demand for the quantity and quality of fruits
satisfying the degree of mastery of fruit farmers' information on large-scale orchards has been difficult for traditional field research methods. Hence
determining how to accurately obtain the same fruit types in different fruit types and different mature states in high-resolution remote sensing orchard images by using remote sensing methods and image-processing methods to obtain orchard distribution and fruit growth information in a timely and rapid manner has become a research focus. Improving the classification accuracy rate effectively is conducive to the dynamic monitoring of large-scale orchard
which has far-reaching significance for promoting the sustainable development of the Chinese fruit industry. In recent years
combining artificial intelligence collection and analysis of high-resolution crop remote sensing images to analyze crop distribution
growth
and parameters has become an important field of agricultural technology development. Sample collection
image data preprocessing
image classification
and sample analysis from crop land data are cumbersome data-mining processes. Traditional machine-learning algorithms are widely used in data-preprocessing stages and image classification. Using traditional threshold segmentation algorithms can effectively classify different fruit types
and wavelet algorithm
support vector machine algorithm
and random forest algorithm as good classifiers can greatly improve the classification accuracy. When neural networks are once again valued and deeply explored
a series of networks
such as convolutional neural networks
deep confidence networks
and adversarial networks
is applied in image classification
segmentation
and recognition. The depth-mining ability of image feature information can be used to obtain a complete feature space effectively. Data with complete feature space and label are easy to be learned by computers to obtain training models
which greatly improve classification accuracy. High-resolution remote sensing image recognition technology
which focuses on crop planting
mechanization
and manorization
has been widely used in crop classification. Using superior depth-learning algorithms to mine high-resolution crop image information is beneficial for the efficient analysis of crop growth and parameter prediction.
Method
2
Atrous convolution is more advantageous than other convolutional networks. It can mine detailed underlying feature information
but can easily cause overfitting and feature redundancy because substantial information space needs to be considered. Popular learning algorithms can effectively perform features. Preliminary classification extracts the feature space that is most conducive to deep learning classification. The depth-separable network and the porous space pyramid can be regarded as feature-encoding processes
and the upsampling process constitutes the backend decoding process. In this study
an improved deep neural network (DeepLab) high-resolution orchard remote sensing image segmentation algorithm is proposed. First
the polarization characteristics of the original data
the features based on coherent decomposition
and the features based on incoherent decomposition are used to form a high-dimensional feature space. Then
the popular learning dimension reduction method is used to obtain the optimal three-channel feature vector to form a pseudo-color map
and a depth separable network (xception)
a cavity convolution network (atrous convolution)
atrous spatial pyramid pooling (ASPP)
and upsample are adopted to build the encoder-decoder of DeepLab. Finally
the pseudo-color training set and the label are imported into the constructed DeepLab to train and save the model
which can be used to effectively classify the target data.
Result
2
The proposed algorithm can be used to classify five types of fruits
namely
mango
phase Ⅱ mango
phase Ⅲ mango
betel nut
and longan
in a certain area of Hainan
China. The error rate of the same fruit classification for different periods decreases by approximately 8% according to the high-resolution image feature information-learning process of mango
betel nut
and longan. Compared with the traditional orchard classification algorithm
the proposed algorithm presents increased kappa coefficient by approximately 0.1 and improved overall classification accuracy to some extent. The proposed algorithm not only has considerable effects on different types of fruit classification but also a more accurate sample division effect in different periods of the same fruit.
Conclusion
2
The algorithm improves the classification accuracy of the same type of fruit in different periods on the basis of preserving the classification accuracy of different types of fruits. The accuracy of crop growth analysis is improved to a certain extent
and the reliability of high-resolution orchard data analysis is ensured. The DeepLab network is advancing to high-resolution data classification. This network can be feasibly applied to determine the status of rice development in different periods in the future because of its superiority in the analysis of different maturity states of the same species. The health of large areas of rice can be monitored.
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