Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field

Wang, Shouyi; Xu, Zhijie; Zhang, Chengming; Zhang, Jinghan; Mu, Zhongshan; Zhao, Tianyu; Wang, Yuanyuan; Gao, Shuai; Yin, Hao; Zhang, Ziyun

doi:10.3390/rs12050821

Cited by 9 publications

(7 citation statements)

References 77 publications

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“…In our experiment, the current mainstream network models were selected for comparison, SegNet [58], FCN8s [41,59,60], U-Net [61], Res-U-Net [37], PSPNet [62], RefineNet, and DeepLabv3+ [63][64][65]. These models have been widely used in research on the semantic segmentation of remote sensing images.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Automatic Pear Extraction from High-Resolution Images by a Visual Attention Mechanism Network

et al. 2023

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At present, forest and fruit resource surveys are mainly based on ground surveys, and the information technology of the characteristic forest and fruit industries is evidently lagging. The automatic extraction of fruit tree information from massive remote sensing data is critical for the healthy development of the forest and fruit industries. However, the complex spatial information and weak spectral information contained in high-resolution images make it difficult to classify fruit trees. In recent years, fully convolutional neural networks (FCNs) have been shown to perform well in the semantic segmentation of remote sensing images because of their end-to-end network structures. In this paper, an end-to-end network model, Multi-Unet, was constructed. As an improved version of the U-Net network structure, this structure adopted multiscale convolution kernels to learn spatial semantic information under different receptive fields. In addition, the “spatial-channel” attention guidance module was introduced to fuse low-level and high-level features to reduce unnecessary semantic features and refine the classification results. The proposed model was tested in a characteristic high-resolution pear tree dataset constructed through field annotation work. The results show that Multi-Unet was the best performer among all models, with classification accuracy, recall, F1, and kappa coefficient of 88.95%, 89.57%, 89.26%, and 88.74%, respectively. This study provides important practical significance for the sustainable development of the characteristic forest fruit industry.

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Section: Experiments and Resultsmentioning

confidence: 99%

Automatic Pear Extraction from High-Resolution Images by a Visual Attention Mechanism Network

et al. 2023

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“…This was followed by PSPNet (ResNet-50 Backbone), DeepLab V3+ (Xception Backbone), U-net (VGG-16 Backbone), and DeepLab V3+ (ResNet-50 Backbone). Numerous studies that have used and compared different deep semantic segmentation architectures for tree, crop, and vegetation mapping have reported similar ranges of segmentation metrics [57,70,100,102,117,125]. For instance, five semantic segmentation architectures, including SegNet, U-Net, FC-DenseNet, and DeepLab V3+( based on Xception and MobileNetV2 backbones), were evaluated in Reference [100] for segmenting threatened single tree species from UAV-based images.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, semantic segmentation, a commonly used term in computer vision where each pixel within the input imagery is assigned to a particular class, has been a widely used technique in diverse earth-related applications [108]. Various architectures, such as fully convolutional networks (FCNs), SegNet [109], U-Net [110], and DeepLab V3+ [111], have been used successfully to delineate tree and vegetation species [70,98,100,101,103,105,106,[112][113][114][115][116][117][118][119][120][121][122][123][124], crops [51,57,58,102,125,126], wetlands [107,127], and weeds [61,99] from various remotely sensed data. For instance, Freudenberg et al [128] utilized U-Net architecture to detect oil and coconut palms from WorldView 2, 3 satellite images.…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

et al. 2021

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Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.

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“…Ji et al [58] proposed a 3D FCN embedded with global pool and channel attention modules, which extracted the spatiotemporal features of different crop types from multi-temporal high-resolution satellite images. Wang et al [59] utilized the China GF-2 remote-sensing satellite to acquire winter wheat images, and developed the RefineNet-PCCCRF model, which accurately extracted the large-scale spatial distribution of winter wheat.…”

Section: Classification Of Crop Production Plotsmentioning

confidence: 99%

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

et al. 2023

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The categorization and identification of agricultural imagery constitute the fundamental requisites of contemporary farming practices. Among the various methods employed for image classification and recognition, the convolutional neural network (CNN) stands out as the most extensively utilized and swiftly advancing machine learning technique. Its immense potential for advancing precision agriculture cannot be understated. By comprehensively reviewing the progress made in CNN applications throughout the entire crop growth cycle, this study aims to provide an updated account of these endeavors spanning the years 2020 to 2023. During the seed stage, classification networks are employed to effectively categorize and screen seeds. In the vegetative stage, image classification and recognition play a prominent role, with a diverse range of CNN models being applied, each with its own specific focus. In the reproductive stage, CNN’s application primarily centers around target detection for mechanized harvesting purposes. As for the post-harvest stage, CNN assumes a pivotal role in the screening and grading of harvested products. Ultimately, through a comprehensive analysis of the prevailing research landscape, this study presents the characteristics and trends of current investigations, while outlining the future developmental trajectory of CNN in crop identification and classification.

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Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field

Cited by 9 publications

References 77 publications

Automatic Pear Extraction from High-Resolution Images by a Visual Attention Mechanism Network

Automatic Pear Extraction from High-Resolution Images by a Visual Attention Mechanism Network

Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

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