• Laser & Optoelectronics Progress
  • Vol. 60, Issue 2, 0228009 (2023)
Mengjia Niu1, Yongjun Zhang1,*, Zhi Li1, Gang Yang2..., Zhongwei Cui3 and Junwen Liu1|Show fewer author(s)
Author Affiliations
  • 1College of Computer Science and Technology, Guizhou University, Guiyang 550025, Guizhou, China
  • 2Guiyang Orbita Aerospace Science&Technology Co., Ltd., Guiyang 550027, Guizhou, China
  • 3Big Data Science and Intelligent Engineering Research Institute, Guizhou Education University, Guiyang 550018, Guizhou, China
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    DOI: 10.3788/LOP220525 Cite this Article Set citation alerts
    Mengjia Niu, Yongjun Zhang, Zhi Li, Gang Yang, Zhongwei Cui, Junwen Liu. Remote Sensing Image Segmentation Network Based on Adaptive Multiscale and Contour Gradient[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0228009 Copy Citation Text show less
    References

    [1] Sishodia R P, Ray R L, Singh S K. Applications of remote sensing in precision agriculture: a review[J]. Remote Sensing, 12, 3136(2020).

    [2] Diakogiannis F I, Waldner F, Caccetta P et al. ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 94-114(2020).

    [3] Liu J X, Ban W, Chen Y et al. Multi-dimensional CNN fused algorithm for hyperspectral remote sensing image classification[J]. Chinese Journal of Lasers, 48, 1610003(2021).

    [4] Liu F S, Wang Q. A sparse tensor-based classification method of hyperspectral image[J]. Signal Processing, 168, 107361(2020).

    [5] Gong X, Chen Z L, Wu L et al. Transfer learning based mixture of experts classification model for high-resolution remote sensing scene classification[J]. Acta Optica Sinica, 41, 2301003(2021).

    [6] Zhu S X, Zhou Z J, Gu X J et al. Scene classification of remote sensing images based on RCF network[J]. Laser & Optoelectronics Progress, 58, 1401001(2021).

    [7] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

    [8] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

    [9] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

    [10] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 833-851(2018).

    [11] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

    [12] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

    [13] Yang M K, Yu K, Zhang C et al. DenseASPP for semantic segmentation in street scenes[C], 3684-3692(2018).

    [14] Yuan Y H, Chen X L, Wang J D. Object-contextual representations for semantic segmentation[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020, 12351, 173-190(2020).

    [15] Li Z B, Shi W Z, Wang Q M et al. Extracting man-made objects from high spatial resolution remote sensing images via fast level set evolutions[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 883-899(2015).

    [16] Liasis G, Stavrou S. Building extraction in satellite images using active contours and colour features[J]. International Journal of Remote Sensing, 37, 1127-1153(2016).

    [17] Cheng D C, Meng G F, Xiang S M et al. FusionNet: edge aware deep convolutional networks for semantic segmentation of remote sensing harbor images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 5769-5783(2017).

    [18] Marmanis D, Schindler K, Wegner J D et al. Classification with an edge: improving semantic image segmentation with boundary detection[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 135, 158-172(2018).

    [19] Takikawa T, Acuna D, Jampani V et al. Gated-SCNN: gated shape CNNs for semantic segmentation[C], 5228-5237(2019).

    [20] Zhu Q, Liao C, Hu H et al. MAP-net: multiple attending path neural network for building footprint extraction from remote sensed imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 6169-6181(2021).

    [22] Ji S P, Wei S Q, Lu M. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 574-586(2019).

    [23] Romera E, Álvarez J M, Bergasa L M et al. ERFNet: efficient residual factorized ConvNet for real-time semantic segmentation[J]. IEEE Transactions on Intelligent Transportation Systems, 19, 263-272(2018).

    [24] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

    [25] Cao Z Y, Fu K, Lu X D et al. End-to-end DSM fusion networks for semantic segmentation in high-resolution aerial images[J]. IEEE Geoscience and Remote Sensing Letters, 16, 1766-1770(2019).

    [26] Zhang X J, Wang X L. Image segmentation models of remote sensing using full residual connection and multiscale feature fusion[J]. Journal of Remote Sensing, 24, 1120-1133(2020).

    [27] Nong Z X, Su X, Liu Y et al. Boundary-aware dual-stream network for VHR remote sensing images semantic segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 5260-5268(2021).

    [28] Liu H, Luo J C, Huang B et al. DE-net: deep encoding network for building extraction from high-resolution remote sensing imagery[J]. Remote Sensing, 11, 2380(2019).

    [29] Wei S Q, Ji S P, Lu M. Toward automatic building footprint delineation from aerial images using CNN and regularization[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 2178-2189(2020).

    Mengjia Niu, Yongjun Zhang, Zhi Li, Gang Yang, Zhongwei Cui, Junwen Liu. Remote Sensing Image Segmentation Network Based on Adaptive Multiscale and Contour Gradient[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0228009
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