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Feature extraction and recognition of erosion gully based on remote sensing image in the black soil region in Northeast China

  • role： First author第一作者
  • Email：yupeixin19921227@126.com
  • Introduction：于佩鑫，1992年生，女，硕士研究生，研究方向为植被遥感和遥感图像分类。E-mail： yupeixin19921227@126.com
Peixin YU,  
  • No information about the author is available
Xun ZHOU,  
  • role： Corresponding author通信作者
  • Email：liush@bnu.edu.cn
  • Introduction：刘素红，1967年生，女，教授，研究方向为植被遥感和遥感图像解译。E-mail： liush@bnu.edu.cn
Suhong LIU*,  
  • No information about the author is available
Xikai WANG

résumé

Northeast China is a major commodity grain base in China. The protection of cultivated lands in Northeast China is crucial for safeguarding the food security in China. The recognition of erosion gullies is an important means of monitoring soil erosion. Furthermore, remote sensing technology is extensively used in this field given the multiple advantages of this technology. However, the traditional methods based on remote sensing mostly depend on manual interpretations. Therefore, the degree of the automation and the efficiency are relatively low. In this study, multi-level features were extracted, thereby effectively describing the specific objects by using machine and deep learnings, and erosion gullies were identified based on these features to improve the accuracy and efficiency of recognizing erosion gullies. In this study, we first cut the remote sensing images in a fixed size and labeled these images manually to create datasets as training samples that consist of two categories, namely, farmland and erosion gully. Second, we extracted spectral and textural features based on this dataset as low-level features, encoded SIFT features through ScSPM as middle-level features, and extracted high-level features by using CNN. Third, a linear SVM and a softmax classifier were applied to classify the remote sensing images based on the multi-level features to identify the images with erosion gullies. Finally, we completed a set of methods to extract the feature and recognize the erosion gully, thereby providing a robust support for protecting arable land in the black soil area of Northeast China. The multi-level features extracted through the proposed method demonstrate specific capabilities in identifying erosion gully images. In the test phase, results show that the recognition outcome based on low-level features exhibits the lowest accuracy (91.1%), whereas the recognition accuracy based on middle-level features is the highest (98.5%). However, both features require a manual design. Hence, the degree of automation is limited. By contrast, the CNN can extract high-level features and automatically achieve an “end-to-end” learning, which highly improves the degree of automation of erosion gully recognition. Furthermore, the recognition accuracy based on high-level features is 95.5%, which satisfies the expectation of this study. The recognition accuracy is slightly lower in the validation phase than in the test phase because the images typically contain several irrelevant objects in the practical application, thereby preventing the improvement of the accuracy. However, the proposed method can generally identify the erosion gullies in the images with a reasonable practicality. Low-level features demonstrate several advantages, such as simple calculation and low time consumed. However, the capability to describe the erosion gully is relatively poor, thus resulting in low recognition accuracy. By contrast, the methods based on middle- and high-level features can identify nearly all the erosion gullies in the images, although these methods are time-consuming during the early training phase. Specifically, the method based on high-level features can automatically recognize the erosion gully. This study shows that deep learning has a great potential in remote sensing image application. If the sample size is continuously increased and the network structure expanded, then the recognition accuracy of erosion gully can be further improved.

mots-clés

erosion gullies;training samples;low-level features;middle-level features;high-level features;convolution neural network

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