DenseNet-Based Land Cover Classification Network With Deep Fusion

Shan, Lianlei; Wang, Weiqiang

doi:10.1109/lgrs.2020.3042199

Cited by 30 publications

(14 citation statements)

References 20 publications

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“…Also, it empowers feature reusing and improves feature progression. Furthermore, DenseNet decreases the sum of parameters significantly [ [76] , [77] , [78] ].…”

Section: Methodsmentioning

confidence: 99%

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

Attallah

2023

Chemometrics and Intelligent Laboratory Systems

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“…Also, it empowers feature reusing and improves feature progression. Furthermore, DenseNet decreases the sum of parameters significantly [ [76] , [77] , [78] ].…”

Section: Methodsmentioning

confidence: 99%

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

Attallah

2023

Chemometrics and Intelligent Laboratory Systems

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“…The CNN learning session shows that its performance is improved compared with the model using traditional methods. DenseNet [9,10,11,12] and DenseCNN have made considerable improvements in capturing user interest, which greatly improves the performance of the model. In our method, sessions are represented as graphs and GNN is used to learn object representation.…”

Section: The Data Setmentioning

confidence: 99%

Graph convolutional neural network for individual consumption behavior analysis

Vincent¹

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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“…Along with the rapid development of deep learning-based methods for semantic segmentation of natural images, interest is growing in designing deep models for land-cover classification of remote sensing images. However, the complex spectrum and irregular boundaries of ground objects in remote sensing images pose a considerable challenge to the robust learning of ground objects for land-cover classification [35]. Contextual information refers to the relationship between pixels and objects [36], which serves as a significant cue for correct recognition of ground objects and coherent labeling of land-cover categories.…”

Section: A Land-cover Classificationmentioning

confidence: 99%

Semantic-Aware Region Loss for Land-Cover Classification

Zheng

Huan

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Integrating superpixel segmentation into convolutional neural networks is known to be effective in enhancing the accuracy of land-cover classification. However, most of existing methods accomplish such integration by focusing on the development of new network architectures, which suffer from several flaws: 1) conflicts between general superpixels and semantic labels introduce noise into the training, especially at object boundaries; 2) absence of training guidance for superpixels leads to ineffective regional feature learning; 3) unnecessary superpixel segmentation in the testing stage not only increases the computational burden but also incurs jagged edges. In this study, we propose a novel semantic-aware region (SARI) loss to guide the effective learning of regional features with superpixels for accurate land-cover classification. The key idea of the proposed method is to reduce the feature variance inside and between homogeneous superpixels while enlarging feature discrepancy between heterogeneous ones. The SARI loss is thus designed with three sub-parts, including superpixel variance loss, intra-class similarity loss and interclass distance loss. We also develop semantic superpixels to assist in the network training with SARI loss while overcoming the limitations of general superpixels. Extensive experiments on two challenging datasets demonstrate that the SARI loss can facilitate regional feature learning, achieving state-of-the-art performance with mIoU scores of around 97.11% and 73.99%

show abstract

DenseNet-Based Land Cover Classification Network With Deep Fusion

Cited by 30 publications

References 20 publications

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

Graph convolutional neural network for individual consumption behavior analysis

Semantic-Aware Region Loss for Land-Cover Classification

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