RGB-D Semantic Segmentation: A Review

Hu, Yaosi; Chen, Zhenzhong; Lin, Weiyao

doi:10.1109/icmew.2018.8551554

Cited by 20 publications

(11 citation statements)

References 13 publications

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“…In the experimental dataset shown in Table 1, the segmentation algorithm in references [4,6,10,12], which are named as Seg-MF, Seg-U-Net, Seg-U-Like-Net, Seg-GMM, respectively, and the segmentation algorithm designed in this paper, Seg-GCN, are used to carry out experiments, and the performance of the three segmentation algorithms are compared.…”

Section: Simulation Experimentsmentioning

confidence: 99%

A Segmentation Algorithm of Image Semantic Sequence Data Based on Graph Convolution Network

Jia

Chen

2021

Security and Communication Networks

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Image semantic data have multilevel feature information. In the actual segmentation, the existing segmentation algorithms have some limitations, resulting in the fact that the final segmentation accuracy is too small. To solve this problem, a segmentation algorithm of image semantic sequence data based on graph convolution network is constructed. The graph convolution network is used to construct the image search process. The semantic sequence data are extracted. After the qualified data points are accumulated, the gradient amplitude forms complete rotation field and no scatter field in the diffusion process, which enhances the application scope of the algorithm, controls the accuracy of the segmentation algorithm, and completes the construction of the data segmentation algorithm. After the experimental dataset is prepared and the semantic segmentation direction is defined, we compare our method with four methods. The results show that the segmentation algorithm designed in this paper has the highest accuracy.

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Section: Simulation Experimentsmentioning

confidence: 99%

A Segmentation Algorithm of Image Semantic Sequence Data Based on Graph Convolution Network

Jia

Chen

2021

Security and Communication Networks

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“…The higher OA value, the more correctly classified samples among the entire samples among the dataset. IoU (Equation (2)) was a parameter calculated by the number of samples intersection divided by that of union [64], or the ratio between the number of true positive and the sum of the number of true positives, false positives, false negatives [65]. The higher IoU of ith class (IoU i ), the fewer samples with wrong predictions or misclassified of ith class.…”

Section: Semantic Segmentation Performance Evaluationmentioning

confidence: 99%

Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

et al. 2021

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Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management.

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“…Semantic segmentation is defined as a multi-label classification problem (Hu et al, 2018), which aims to assign category labels to each pixel on the image. It contains two tasks, image segmentation and target recognition.…”

Section: Introductionmentioning

confidence: 99%

“…There are two groups of semantic segmentation methods for the remote sensing imagery, traditional method and deep learning method (Hu et al, 2018). The deep learning method mainly utilizes Convolution Neural Network (CNN) (Pedro H. O., Collobert, 2013, Yu et al, 2018, Fully Convolutional Network (FCN) (Long et al, 2014 and other improved neural network methods (Paszke et al, 2016, Kampffmeyer et al, 2016 to extract features and obtain semantic segmentation results.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Remote Sensing Imagery Using Object-Based Markov Random Field Based on Hierarchical Segmentation Tree With Auxiliary Labels

Zhang

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In the remote sensing imagery, spectral and texture features are always complex due to different landscapes, which leads to misclassifications in the results of semantic segmentation. The object-based Markov random field provides an effective solution to this problem. However, the state-of-the-art object-based Markov random field still needs to be improved. In this paper, an object-based Markov Random Field model based on hierarchical segmentation tree with auxiliary labels is proposed. A remote sensing imagery is first segmented and the object-based hierarchical segmentation tree is built based on initial segmentation objects and merging criteria. And then, the object-based Markov random field with auxiliary label fields is established on the hierarchical tree structure. A probabilistic inference is applied to solve this model by iteratively updating label field and auxiliary label fields. In the experiment, this paper utilized a Worldview-3 image to evaluate the performance, and the results show the validity and the accuracy of the presented semantic segmentation approach.

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RGB-D Semantic Segmentation: A Review

Cited by 20 publications

References 13 publications

A Segmentation Algorithm of Image Semantic Sequence Data Based on Graph Convolution Network

A Segmentation Algorithm of Image Semantic Sequence Data Based on Graph Convolution Network

Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

Semantic Segmentation of Remote Sensing Imagery Using Object-Based Markov Random Field Based on Hierarchical Segmentation Tree With Auxiliary Labels

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