Publication: Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery
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Issued Date
2023
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application/pdf
Rights Holder(s)
Scopus
Bibliographic Citation
ISPRS International Journal of Geo-Information. Vol 12, No.1 (2023)
Suggested Citation
Boonpook W., Tan Y., Nardkulpat A., Torsri K., Torteeka P., Kamsing P., Sawangwit U., Pena J., Jainaen M. Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery. ISPRS International Journal of Geo-Information. Vol 12, No.1 (2023). doi:10.3390/ijgi12010014 Retrieved from: https://hdl.handle.net/20.500.14740/10741
Other Contributor(s)
Abstract
Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem in small land use features. In this paper, a deep learning semantic segmentation algorithm which comprises an adjustment network architecture (LoopNet) and land use dataset is proposed for automatic land use classification using Landsat 8 imagery. The experimental results illustrate that deep learning semantic segmentation using the baseline network (SegNet, U-Net) outperforms pixel-based machine learning algorithms (MLE, SVM, RF) for land use classification. Furthermore, the LoopNet network, which comprises a convolutional loop and convolutional block, is superior to other baseline networks (SegNet, U-Net, PSPnet) and improvement networks (ResU-Net, DeeplabV3+, U-Net++), with 89.84% overall accuracy and good segmentation results. The evaluation of multispectral bands in the land use dataset demonstrates that Band 5 has good performance in terms of extraction accuracy, with 83.91% overall accuracy. Furthermore, the combination of different spectral bands (Band 1–Band 7) achieved the highest accuracy result (89.84%) compared to individual bands. These results indicate the effectiveness of LoopNet and multispectral bands for land use classification using Landsat 8 imagery. © 2023 by the authors.