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Journals / International Journal of Engineering and Geosciences / 2021 / Cilt: 6 - Sayı: 1

Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning

Zouhair BENBAHRIA
İmane SEBARİ
Hicham HAJJİ
Mohamed Faouzi SMİEJ
Pages
40–50
Publication Date
2022-10-19
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In the first part of this study, we compared the use of three optimization methods: Adam and two variants of Stochastic Gradient Descent (SGD) associated with two techniques (Cyclical Learning Rate and Warm Restart) to find the optimal learning rate and then test the impact of data augmentation on the overall accuracies.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The lack of reliable and up-to-date data in developing countries is a major obstacle to sustainable development.
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Key Findings & Data:In this paper a novel transfer learning algorithm is proposed to map irrigated areas at different stages of an agricultural cycle from Landsat 8 images.
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Conclusion & Impact:In the first part of this study, we compared the use of three optimization methods: Adam and two variants of Stochastic Gradient Descent (SGD) associated with two techniques (Cyclical Learning Rate and Warm Restart) to find the optimal learning rate and then test the impact of data augmentation on the overall accuracies.
🔬Design:Modeling & Simulation
📍Scope:2012 Verileri
⚙️Variables:Irrigated areas mapping · Landsat 8 · Deep learning

Abstract

The lack of reliable and up-to-date data in developing countries is a major obstacle to sustainable development. In Morocco, where groundwater withdrawals by farmers are very intensive and informal, maps describing and monitoring the extension of irrigated areas are scarce and labor-intensive to obtain. In this paper a novel transfer learning algorithm is proposed to map irrigated areas at different stages of an agricultural cycle from Landsat 8 images. The results obtained displays satisfactory performance over traditional machine learning algorithms. On a small dataset, we initially tested three well known deep learning architectures (SegNet, DenseNet and Unet). The results obtained were not satisfactory. So, to get high performance, we rely on a transfer learning architecture combining UNet with ResNet50 backbone (trained on 2012 ILSVRC ImageNet dataset) as a baseline after a phase where different configurations were tested. In the first part of this study, we compared the use of three optimization methods: Adam and two variants of Stochastic Gradient Descent (SGD) associated with two techniques (Cyclical Learning Rate and Warm Restart) to find the optimal learning rate and then test the impact of data augmentation on the overall accuracies. Data augmentation had improved the overall accuracy for the three methods. Adam based method from 94% to 97% with mean IoU of 0,79 (for all land cover classes) and 0,86 for irrigated areas class. For SGD based methods, the overall accuracy had increased from 91% to 94% with mean IoU of 0,75 (for all land cover classes) and 0,82 for irrigated areas class. As we are interested in having irrigated areas maps at different key periods of the agricultural cycle, we also explored, in the second part of this study, the temporal generalization of the best model.

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Cite This Article

BENBAHRIA, Z., SEBARİ, İ., HAJJİ, H., SMİEJ, M. F. (2022). Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning. International Journal of Engineering and Geosciences, Cilt: 6 - Sayı: 1, 40–50. https://ajindex.com/articles/540683

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References (9)

  1. Acquarelli J, Marchiori E, Buydens L M C, Tran T & van Laarhoven T (2018). Spectral-spatial classification of hyperspectral images: Three tricks and a new learning setting. Remote Sensing, 10(7), 1156. DOI: 10.3390/rs10071156 Audebert N, Le Saux, B & Lefèvre S (2017). Fusion of heterogeneous data in convolutional networks for urban semantic labeling. 2017 Joint Urban Remote Sensing Event (JURSE), 1-4.
  2. Audebert, N, Le Saux B & Lefèvre S (2016). Semantic segmentation of earth observation data using multimodal and multi-scale deep networks. Asian Conference on Computer Vision. Springer, Cham, 180-196.
  3. Badrinarayanan V, Kendall A & Cipolla R (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, 39(12), 2481-2495.
  4. Ball J E, Anderson D T & Wei P (2018). State-of-the-art and gaps for deep learning on limited training data in remote sensing. IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 4119-4122.
  5. Benbahria Z, Sebari I, Hajji H & Smiej M F (2018). Automatic mapping of irrigated areas in Mediterranean context using Landsat 8 time series images and random forest algorithm. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 7986-7989.
  6. Valencia, Spain, DOI: 10.1109/IGARSS.2018.8517810 Bergstra J & Bengio Y (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281–305. Bilinski P & Prisacariu V (2018). Dense decoder shortcut connections for single-pass semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6596-6605.
  7. Bloice M D (2017). https://github.com/mdbloice/Augmentor Accessed date: January, 27th 2020 Diederik P K & Jimmy L B (2014). Adam: A method for stochastic optimization. 3rd International Conference for Learning Representations, San Diego Feng W, Sui H, Huang W, Xu C & An K (2019). Water body extraction from very high-resolution remote sensing imagery using deep U-Net and a superpixel-based conditional random field model. IEEE Geoscience and Remote Sensing Letters, 16(4), 618-622.
  8. Ghazi M M, Yanikoglu B & Aptoula E (2017). Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing, 235, 228-235.
  9. He K, Zhang X, Ren S & Sun J (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770-7