A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net

Liu, Jiahao; Wang, Hong; Zhang, Yao; Zhao, Xiaohui; Qu, Tengfei; Tian, Haozhe; Lu, Yuting; Su, Jingru; Luo, Dehan; Yang, Yi-Yan

doi:10.3390/rs15153711

Cited by 5 publications

(13 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of highresolution satellite imagery significantly enhances the spatial resolution of crop features in images, reducing the occurrence of mixed pixels. This better meets the requirements of precision agriculture management [22,23]. However, high-resolution satellite imagery has weaker spectral information and lower temporal resolution.…”

Section: Introductionmentioning

confidence: 81%

Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network

Chang,

Li,

Chen

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Remote sensing technology has become a popular tool for crop classification, but it faces challenges in accurately identifying crops in areas with fragmented land plots and complex planting structures. To address this issue, we propose an improved method for crop identification in high-resolution remote sensing images, achieved by modifying the DeepLab V3+ semantic segmentation network. In this paper, the typical crop area in the Jianghuai watershed is taken as the experimental area, and Gaofen-2 satellite images with high spatial resolutions are used as the data source. Based on the original DeepLab V3+ model, CI and OSAVI vegetation indices are added to the input layers, and MobileNet V2 is used as the backbone network. Meanwhile, the upper sampling layer of the network is added, and the attention mechanism is added to the ASPP and the upper sampling layers. The accuracy verification of the identification results shows that the MIoU and PA of this model in the test set reach 85.63% and 95.30%, the IoU and F1_Score of wheat are 93.76% and 96.78%, and the IoU and F1_Score of rape are 74.24% and 85.51%, respectively. The identification accuracy of this model is significantly better than that of the original DeepLab V3+ model and other related models. The proposed method in this paper can accurately extract the distribution information of wheat and rape from high-resolution remote sensing images. This provides a new technical approach for the application of high-resolution remote sensing images in identifying wheat and rape.

show abstract

Section: Introductionmentioning

confidence: 81%

Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network

Chang,

Li,

Chen

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…For instance, Wang et al utilized the CBAM to enhance the discriminative capability of ecological environment elements in the Yangtze River source area [45]. Similarly, Liu et al successfully resolved the problem of unclear edges and rough contours in winter wheat extraction by incorporating the CBAM [46]. These enhancements resulted in substantial improvements in training accuracy and efficiency.…”

Section: Model Evaluationmentioning

confidence: 99%

Extracting Citrus in Southern China (Guangxi Region) Based on the Improved DeepLabV3+ Network

Li,

Zhang,

Wang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

China is one of the countries with the largest citrus cultivation areas, and its citrus industry has received significant attention due to its substantial economic benefits. Traditional manual forestry surveys and remote sensing image classification tasks are labor-intensive and time-consuming, resulting in low efficiency. Remote sensing technology holds great potential for obtaining spatial information on citrus orchards on a large scale. This study proposes a lightweight model for citrus plantation extraction that combines the DeepLabV3+ model with the convolutional block attention module (CBAM) attention mechanism, with a focus on the phenological growth characteristics of citrus in the Guangxi region. The objective is to address issues such as inaccurate extraction of citrus edges in high-resolution images, misclassification and omissions caused by intra-class differences, as well as the large number of network parameters and long training time found in classical semantic segmentation models. To reduce parameter count and improve training speed, the MobileNetV2 lightweight network is used as a replacement for the Xception backbone network in DeepLabV3+. Additionally, the CBAM is introduced to extract citrus features more accurately and efficiently. Moreover, in consideration of the growth characteristics of citrus, this study augments the feature input with additional channels to better capture and utilize key phenological features of citrus, thereby enhancing the accuracy of citrus recognition. The results demonstrate that the improved DeepLabV3+ model exhibits high reliability in citrus recognition and extraction, achieving an overall accuracy (OA) of 96.23%, a mean pixel accuracy (mPA) of 83.79%, and a mean intersection over union (mIoU) of 85.40%. These metrics represent an improvement of 11.16%, 14.88%, and 14.98%, respectively, compared to the original DeepLabV3+ model. Furthermore, when compared to classical semantic segmentation models, such as UNet and PSPNet, the proposed model achieves higher recognition accuracy. Additionally, the improved DeepLabV3+ model demonstrates a significant reduction in both parameters and training time. Generalization experiments conducted in Nanning, Guangxi Province, further validate the model’s strong generalization capabilities. Overall, this study emphasizes extraction accuracy, reduction in parameter count, adherence to timeliness requirements, and facilitation of rapid and accurate extraction of citrus plantation areas, presenting promising application prospects.

show abstract

“…The traditional extraction of information on cropland or crops is mainly based on the statistical method of field survey, which requires a large amount of human and material resources [11]. In contrast, agricultural information extraction based on remote sensing technology can achieve near real-time results and can be implemented on a large scale, which greatly improves efficiency and reduces costs [12]. Most of the current remote sensing-based wheat identification methods are implemented from the single pixelbased or object-oriented perspective [13,14].…”

Section: Introductionmentioning

confidence: 99%

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

Feng,

Chen,

Liu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Wheat plays an important role in China’s and the world’s food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources.

show abstract

A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net

Cited by 5 publications

References 52 publications

Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network

Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network

Extracting Citrus in Southern China (Guangxi Region) Based on the Improved DeepLabV3+ Network

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

Contact Info

Product

Resources

About