Seasonal Multi-temporal Pixel Based Crop Types and Land Cover Classification for Satellite Images using Convolutional Neural Networks

Nowadays, Satellite images have become a major source of data for many aspects of development. Land and crops classification using satellite images is a recent important subject. From the other side, Deep Convolutional Neural Networks (DCNNs) is a powerful technique for understanding images. This pa...

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Veröffentlicht in:2018 13th International Conference on Computer Engineering and Systems (ICCES) S. 21 - 26
Hauptverfasser: Laban, Noureldin, Abdellatif, Bassam, Ebeid, Hala M., Shedeed, Howida A., Tolba, Mohamed F.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2018
Schlagworte:
Agriculture
Artificial Intelligence
Convolution
Convolutional neural networks
Crop Classification
Deep learning
Egypt
Feature extraction
Remote Sensing (RS)
Satellite Images
Satellites
Sentinel-2
TensorFlow
Training
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Zusammenfassung:Nowadays, Satellite images have become a major source of data for many aspects of development. Land and crops classification using satellite images is a recent important subject. From the other side, Deep Convolutional Neural Networks (DCNNs) is a powerful technique for understanding images. This paper describes a pixel based crops and land cover classification originating from one source satellite imagery represented by Sentinel satellite and based on several dates for the same agricultural season. We propose a DCNN architecture based on multi-temporal data that was fed to a one-dimension (1-D DCNN). The proposed architecture is compared with other methods of satellite image classification algorithms; such as Support Vector Machines (SVMs), Random Forests (RFs) and k-Nearest Neighbors (k-NNs). Experiments are conducted for the mutual experiment of major crops and land cover classification for Al-Fayoum governorate in Egypt. The 1-D DCNN achieves about 89% accuracy using 10 spectral bands from Sentinel-2 satellite imagery database for the area of interest. The proposed architecture although it outperforms other methods, needs further research to optimize the memory usage.
DOI:10.1109/ICCES.2018.8639232