Densely Based Multi-Scale and Multi-Modal Fully Convolutional Networks for High-Resolution Remote-Sensing Image Semantic Segmentation

Cheng, Peng; Li, Yangyang; Jiao, Licheng; Chen, Yanqiao; Shang, Ronghua

doi:10.1109/jstars.2019.2906387

Cited by 105 publications

(67 citation statements)

References 38 publications

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“…U-Net is symmetric, that is, it has the same number of up-sampling and down-sampling layers. The skip connections in U-Net use a concatenation operator between the up-sampling and down-sampling layers [73]. This method connects the features in the contact path and the extension path.…”

Section: Segmentationmentioning

confidence: 99%

Deep Learning Application in Plant Stress Imaging: A Review

et al. 2020

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Plant stress is one of major issues that cause significant economic loss for growers. The labor-intensive conventional methods for identifying the stressed plants constrain their applications. To address this issue, rapid methods are in urgent needs. Developments of advanced sensing and machine learning techniques trigger revolutions for precision agriculture based on deep learning and big data. In this paper, we reviewed the latest deep learning approaches pertinent to the image analysis of crop stress diagnosis. We compiled the current sensor tools and deep learning principles involved in plant stress phenotyping. In addition, we reviewed a variety of deep learning applications/functions with plant stress imaging, including classification, object detection, and segmentation, of which are closely intertwined. Furthermore, we summarized and discussed the current challenges and future development avenues in plant phenotyping.

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Section: Segmentationmentioning

confidence: 99%

Deep Learning Application in Plant Stress Imaging: A Review

et al. 2020

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“…Yu et al [16] came up with an end-to-end semantic segmentation framework that can simultaneously segment multiple ground objects from HR images. Peng et al [17] came up with dense connection and FCN (DFCN) to automatically acquire fine-grained feature maps of semantic segmentation for HR remote-sensing images. Nogueira et al [18] came up with a novel method based on ConvNets to accomplish semantic segmentation in HR remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery

Wei

Liu

et al. 2020

Remote Sensing

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Instance segmentation in high-resolution (HR) remote sensing imagery is one of the most challenging tasks and is more difficult than object detection and semantic segmentation tasks. It aims to predict class labels and pixel-wise instance masks to locate instances in an image. However, there are rare methods currently suitable for instance segmentation in the HR remote sensing images. Meanwhile, it is more difficult to implement instance segmentation due to the complex background of remote sensing images. In this article, a novel instance segmentation approach of HR remote sensing imagery based on Cascade Mask R-CNN is proposed, which is called a high-quality instance segmentation network (HQ-ISNet). In this scheme, the HQ-ISNet exploits a HR feature pyramid network (HRFPN) to fully utilize multi-level feature maps and maintain HR feature maps for remote sensing images' instance segmentation. Next, to refine mask information flow between mask branches, the instance segmentation network version 2 (ISNetV2) is proposed to promote further improvements in mask prediction accuracy. Then, we construct a new, more challenging dataset based on the synthetic aperture radar (SAR) ship detection dataset (SSDD) and the Northwestern Polytechnical University very-high-resolution 10-class geospatial object detection dataset (NWPU VHR-10) for remote sensing images instance segmentation which can be used as a benchmark for evaluating instance segmentation algorithms in the high-resolution remote sensing images. Finally, extensive experimental analyses and comparisons on the SSDD and the NWPU VHR-10 dataset show that (1) the HRFPN makes the predicted instance masks more accurate, which can effectively enhance the instance segmentation performance of the high-resolution remote sensing imagery; (2) the ISNetV2 is effective and promotes further improvements in mask prediction accuracy; (3) our proposed framework HQ-ISNet is effective and more accurate for instance segmentation in the remote sensing imagery than the existing algorithms.

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“…Experimental results on public scene data sets show that the features extracted by this method can obtain better performance. Peng et al [36] used convolutional networks to identify high-resolution remote sensing images, which reduced the complexity of feature extraction and recognition, and improved recognition accuracy. To solve the problem of optical remote sensing image recognition, Zou et al [37] use the optimized convolutional neural network to recognize the target on the 0.6m resolution remote sensing image.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Remote Sensing Image Information Extraction and Target Recognition Based on Multiple Information Fusion

Liu

Chang

2020

IEEE Access

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The current research on multiple information fusion of remote sensing images is mainly aimed at remote sensing images of specific satellite sensors, and cannot be extended to other types of data source images. For high-resolution remote sensing images, when its surface coverage changes significantly, most of the mainstream algorithms are difficult to restore satisfactorily. The algorithm proposed in this paper combines the sparse representation and the spectral, spatial, and temporal features of remote sensing images for the first time to solve the above problems. The algorithm proposed in this paper first simulates the human visual mechanism, and obtains the spatial, spectral, and temporal features of the remote sensing image through the spatial spectral dictionary learning and the time-varying weight learning model. Secondly, local constraints are added to the extraction of temporal features to obtain temporal and geographical change information of heterogeneous remote sensing images. Then, a sparse representation model combining space-spectrum-time features is proposed to extract features of high-resolution remote sensing images. Finally, based on the VGG-16 network, this paper proposes a target recognition network with deep fully convolutional network, and uses the extracted feature map as the input of the target recognition network to realize the target recognition of the remote sensing image. Experimental results show that the method proposed in this paper can improve the accuracy of target recognition and improve the accuracy of recognition.

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Densely Based Multi-Scale and Multi-Modal Fully Convolutional Networks for High-Resolution Remote-Sensing Image Semantic Segmentation

Cited by 105 publications

References 38 publications

Deep Learning Application in Plant Stress Imaging: A Review

Deep Learning Application in Plant Stress Imaging: A Review

HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery

High-Resolution Remote Sensing Image Information Extraction and Target Recognition Based on Multiple Information Fusion

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