Improving Land-Cover and Crop-Types Classification of Sentinel-2 Satellite Images

Laban, Noureldin; Abdellatif, Bassam; Ebeid, Hala M.; Shedeed, Howida A.; Tolba, Mohamed F.

doi:10.1007/978-3-319-74690-6_44

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…5e, uses a convolutional Neural Network of 1-D kernel size [34]. KNN, RF and SVM, as shown in [35], are all used with best parameters that gave the highest accuracy scores.…”

Section: Results For Sentinel-2 Datamentioning

confidence: 99%

Shallow Parallel CNNs for contextual remote sensing image classification

Abdellatif

Laban

Metwalli

2022

Preprint

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In this paper we present a new neural network structure that can better learn to classify remote sensing images of moderate and high spatial resolution where the main source of information about desired objects are the pixels themselves and the tight neighborhood. It enhances the pixel-based classification process by incorporating the contextual information in its surroundings.The proposed algorithm is an arrangement of small Shallow Parallel Convolutional Neural Network layers, SP-CNN, that are centered, each of them, over training or test pixels. Each SP-CNN drives information from the pixel to be classified and its contextual neighborhood. Depending on the information that may be found in the context of each pixel, the size and the number of SP-CNNs to be used in the proposed structure can be learned or optimized.The proposed method has been applied to Sentinel-2 (10 m resolution) and Pl\'eiades data (0.5 m resolution) and gave superior results when compared to 1-D CNN and other pixel-based methods such as KNN, SVM, and RF.The proposed method shows its suitability when the training and test data are pixel-based and in same time the spatial domain has valuable information to be incorporated in the classification process.

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“…5e, uses a convolutional Neural Network of 1-D kernel size [34]. KNN, RF and SVM, as shown in [35], are all used with best parameters that gave the highest accuracy scores.…”

Section: Results For Sentinel-2 Datamentioning

confidence: 99%

Shallow Parallel CNNs for contextual remote sensing image classification

Abdellatif

Laban

Metwalli

2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…The detailed configuration of comparison methods used is illustrated in our previous work [31] which investigates the best parameters for each method. The results for each one is shown as follows: for k-Nearest Neighbor (k-NN), the best number of neighbors (k) is 3. for Random Forests (RFs), the best number of trees is 50, and the best number of parallel processes is 6.…”

Section: E Comparison With Other Classification Methodsmentioning

confidence: 99%

“…Points Georeferencing Mapping Points on Satellite Images with CNNs frameworks [31]- [35]. Note that all these methods are pixel-based methods and do not use spatial context in their predictions.…”

Section: Working In the Fieldmentioning

confidence: 99%

Sparse Pixel Training of Convolutional Neural Networks for Land Cover Classification

et al. 2021

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Convolutional Neural Networks (CNN) have become the core of modern machine learning approaches. In addition to its inspiring interior design idea, the success of CNN depends mainly on two factors, the first is the availability of training data and the second is the computing power of the used devices. In the field of remote sensing, data availability is difficult and expensive. Furthermore, processing large remote sensing data to accommodate different models is a laborious process. At the same time, training data is often collected in the form of points distributed over crop fields rather than regions, which results in the scarcity of training data. To specifically address the scarcity of training points, in this paper we present a sparse pixel-based training of U-Net convolutional neural networks for land cover classification. Training images are reconstructed from the points' collection in a random manner, they are used as an input for the convolution networks. Based on this proposed method, the amount of training data is reproduced from the different spectral signals for each land cover. We conducted extensive experiments on eight classes, using ground truth data collected from several locations in Fayoum Governorate, Egypt. The obtained results showed the superiority of the proposed method over other methods.

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