发布时间: 2019-11-16 摘要点击次数: 全文下载次数: DOI: 10.11834/jig.190094 2019 | Volume 24 | Number 11 遥感图像处理

1. 湖北工业大学电气与电子工程学院, 武汉 430068;
2. 太阳能高效利用及储能运行控制湖北省重点实验室, 武汉 430068
 收稿日期: 2019-03-20; 修回日期: 2019-05-10; 预印本日期: 2019-05-17 基金项目: 国家自然科学基金项目(41601394);湖北工业大学博士启动基金项目(BSQD2016010) 第一作者简介: 王云艳, 1981年生, 女, 副教授, 主要研究方向为模式识别、SAR图像处理。E-mail:510496148@qq.com;周志刚, 男, 硕士研究生, 主要研究方向为模式识别、SAR图像处理。E-mail:z1441396422@qq.com. 中图法分类号: TP181 文献标识码: A 文章编号: 1006-8961(2019)11-2035-10

# 关键词

Polarized SAR orchard classification based on improved DeepLab
Wang Yunyan1,2, Luo Lengkun1,2, Zhou Zhigang1,2
1. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China;
2. Key Laboratory of High Efficiency Utilization and Energy Storage Operation Control of Hubei Province, Wuhan 430068, China
Supported by: National Natural Science Foundation of China (41601394)

# Abstract

Objective 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 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 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 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.

# Key words

high resolution; atrous convolution; deep learning; porous space pyramid; depth separable network

# 1.2 拉普拉斯降维

1) 构造近邻图，首先连接样本点，连接每个点最近的$k$个点，$k$值为事先设定。

2) 利用热核函数来确定相邻点之间权重，其表达式为

 $w=\exp \left(-\frac{\left\|x_{1}-x_{2}\right\|^{2}}{t}\right)$ (1)

 $OA = \frac{{\sum\limits_{i = 1}^K {{P_{aa}}} }}{{\sum\limits_{i = 1}^K {{t_a}} }}$ (8)

 $A c=\frac{P_{a a}}{t_{a}}$ (9)

$κ$$OA$和类特定精度值$Ac$在01之间，值越高，分类性能越好。

# 3.3 实验结果

Table 1 DeepLab class partitioning confusion matrix

 /% Ⅰ期芒果 Ⅱ期芒果 Ⅲ期芒果 槟榔 龙眼 Ⅰ期芒果 98.56 1.20 0.61 0.00 0.00 Ⅱ期芒果 1.30 98.33 0.43 0.00 0.00 Ⅲ期芒果 3.10 1.52 95.62 0.00 0.00 槟榔 0.00 0.00 0.10 99.23 0.56 龙眼 0.00 0.33 0.20 0.72 98.32 注：加粗字体为每类分割正确的概率。

Table 2 Evaluation indicators

 方法 κ OA/% Ac/% Ⅰ期芒果 Ⅱ期芒果 Ⅲ期芒果 槟榔 龙眼 GLCM+SVM 0.65 74.09 78.39 64.56 82.13 80.01 65.34 decomposition+SPM 0.61 64.16 61.33 55.83 86.64 58 59 SDU-CNN 0.76 80 76.17 79.56 84.48 73.21 86.6 DeepLab 0.89 91.14 95.62 91.56 85.62 94.33 88.56 改进型DeepLab 0.96 98.01 98.56 98.33 95.62 99.23 98.32 注：加粗字体表示最佳结果。

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