An entropy and MRF model-based CNN for large-scale Landsat image classification

Zhao, Xuemei and Gao, Lianru and Chen, Zhengchao and Zhang, Bing and Liao, Wenzhi (2019) An entropy and MRF model-based CNN for large-scale Landsat image classification. IEEE Geoscience and Remote Sensing Letters, 16 (7). pp. 1145-1149. ISSN 1545-598X (https://doi.org/10.1109/LGRS.2019.2890996)

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Abstract

Large-scale Landsat image classification is essential for the production of land cover maps. The rise of convolutional neural networks (CNNs) provides a new idea for the implementation of Landsat image classification. However, pixels in Landsat images have higher uncertainty compared with high-resolution images due to its 30-m spatial resolution. In addition, the current deep learning methods tend to lose detailed information such as boundaries along with the stacking of convolutional and pooling layers. To solve these problems, we propose a new method called entropy and MRF model (EMM)-CNN based on Pyramid Scene Parsing Network. The EMM-CNN uses entropy to decrease the uncertainty of pixels. Then, the Markov random filed (MRF) model is employed to construct the connections between neighboring pixels and defined a prior distribution to prevent the cross entropy from sacrificing detailed information for the overall accuracy. Finally, transfer learning based on the pretrained ImageNet is introduced to overcome the shortage of training samples and boost the speed of the training process. Experimental results demonstrate that the proposed EMM-CNN is able to obtain classification results with fine structure by decreasing the uncertainty and retaining detailed information of the detected image.

  • Item type:Article
    ID code:69456
    Dates:
    Date
    Event
    31 July 2019
    Published
    24 January 2019
    Published Online
    24 December 2018
    Accepted
    Notes:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:21 Aug 2019 14:29
    Last modified:11 Nov 2024 12:24
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/69456
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