scholarly journals Mapping Plastic Mulched Farmland for High Resolution Images of Unmanned Aerial Vehicle Using Deep Semantic Segmentation

2019 ◽  
Vol 11 (17) ◽  
pp. 2008 ◽  
Author(s):  
Qinchen Yang ◽  
Man Liu ◽  
Zhitao Zhang ◽  
Shuqin Yang ◽  
Jifeng Ning ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Classification Method ◽  
Remote Sensing Images ◽  
Segmentation Methods ◽  
Traditional Classification ◽  
Aerial Vehicle ◽  
Segmentation Models

With increasing consumption, plastic mulch benefits agriculture by promoting crop quality and yield, but the environmental and soil pollution is becoming increasingly serious. Therefore, research on the monitoring of plastic mulched farmland (PMF) has received increasing attention. Plastic mulched farmland in unmanned aerial vehicle (UAV) remote images due to the high resolution, shows a prominent spatial pattern, which brings difficulties to the task of monitoring PMF. In this paper, through a comparison between two deep semantic segmentation methods, SegNet and fully convolutional networks (FCN), and a traditional classification method, Support Vector Machine (SVM), we propose an end-to-end deep-learning method aimed at accurately recognizing PMF for UAV remote sensing images from Hetao Irrigation District, Inner Mongolia, China. After experiments with single-band, three-band and six-band image data, we found that deep semantic segmentation models built via single-band data which only use the texture pattern of PMF can identify it well; for example, SegNet reaching the highest accuracy of 88.68% in a 900 nm band. Furthermore, with three visual bands and six-band data (3 visible bands and 3 near-infrared bands), deep semantic segmentation models combining the texture and spectral features further improve the accuracy of PMF identification, whereas six-band data obtains an optimal performance for FCN and SegNet. In addition, deep semantic segmentation methods, FCN and SegNet, due to their strong feature extraction capability and direct pixel classification, clearly outperform the traditional SVM method in precision and speed. Among three classification methods, SegNet model built on three-band and six-band data obtains the optimal average accuracy of 89.62% and 90.6%, respectively. Therefore, the proposed deep semantic segmentation model, when tested against the traditional classification method, provides a promising path for mapping PMF in UAV remote sensing images.

scholarly journals Depth Semantic Segmentation of Tobacco Planting Areas from Unmanned Aerial Vehicle Remote Sensing Images in Plateau Mountains

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Liang Huang ◽  
Xuequn Wu ◽  
Qiuzhi Peng ◽  
Xueqin Yu
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Network Models ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Training Time ◽  
Backbone Networks ◽  
Aerial Vehicle ◽  
Model Training ◽  
Self Learning

The tobacco in plateau mountains has the characteristics of fragmented planting, uneven growth, and mixed/interplanting of crops. It is difficult to extract effective features using an object-oriented image analysis method to accurately extract tobacco planting areas. To this end, the advantage of deep learning features self-learning is relied on in this paper. An accurate extraction method of tobacco planting areas based on a deep semantic segmentation model from the unmanned aerial vehicle (UAV) remote sensing images in plateau mountains is proposed in this paper. Firstly, the tobacco semantic segmentation dataset is established using Labelme. Four deep semantic segmentation models of DeeplabV3+, PSPNet, SegNet, and U-Net are used to train the sample data in the dataset. Among them, in order to reduce the model training time, the MobileNet series of lightweight networks are used to replace the original backbone networks of the four network models. Finally, the predictive images are semantically segmented by trained networks, and the mean Intersection over Union (mIoU) is used to evaluate the accuracy. The experimental results show that, using DeeplabV3+, PSPNet, SegNet, and U-Net to perform semantic segmentation on 71 scene prediction images, the mIoU obtained is 0.9436, 0.9118, 0.9392, and 0.9473, respectively, and the accuracy of semantic segmentation is high. The feasibility of the deep semantic segmentation method for extracting tobacco planting surface from UAV remote sensing images has been verified, and the research method can provide a reference for subsequent automatic extraction of tobacco planting areas.

scholarly journals Improved Anchor-Free Instance Segmentation for Building Extraction from High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (18) ◽  
pp. 2910
Author(s):  
Tong Wu ◽  
Yuan Hu ◽  
Ling Peng ◽  
Ruonan Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Highly Sensitive ◽  
Segmentation Methods ◽  
Speed And Accuracy ◽  
Instance Segmentation

Building extraction from high-resolution remote sensing images plays a vital part in urban planning, safety supervision, geographic databases updates, and some other applications. Several researches are devoted to using convolutional neural network (CNN) to extract buildings from high-resolution satellite/aerial images. There are two major methods, one is the CNN-based semantic segmentation methods, which can not distinguish different objects of the same category and may lead to edge connection. The other one is CNN-based instance segmentation methods, which rely heavily on pre-defined anchors, and result in the highly sensitive, high computation/storage cost and imbalance between positive and negative samples. Therefore, in this paper, we propose an improved anchor-free instance segmentation method based on CenterMask with spatial and channel attention-guided mechanisms and improved effective backbone network for accurate extraction of buildings in high-resolution remote sensing images. Then we analyze the influence of different parameters and network structure on the performance of the model, and compare the performance for building extraction of Mask R-CNN, Mask Scoring R-CNN, CenterMask, and the improved CenterMask in this paper. Experimental results show that our improved CenterMask method can successfully well-balanced performance in terms of speed and accuracy, which achieves state-of-the-art performance at real-time speed.

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

scholarly journals Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

2021 ◽  
Vol 13 (13) ◽  
pp. 2524
Author(s):  
Ziyi Chen ◽  
Dilong Li ◽  
Wentao Fan ◽  
Haiyan Guan ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Extraction Methods ◽  
The Self ◽  
Optical Remote Sensing ◽  
Building Extraction ◽  
Learning Models ◽  
Remote Sensing Images ◽  
Segmentation Methods

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

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