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

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

doi:10.1109/icces.2018.8639232

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…The authors in [50] proposed a seasonal land cover and crop classification approach using the Deep CNN (DCNN) architecture. Their work investigated the pixel-based crops and land cover classification on several dates for the same agricultural season from the Sentinel satellite.…”

Section: Deep Learning Techniques For Hyperspectral Data Analyticsmentioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.

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Section: Deep Learning Techniques For Hyperspectral Data Analyticsmentioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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“…The same training and test sets have been used in all experiments. The used algorithms are 1-D CNN [34], K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM) along with the proposed SP-CNN. To distinguish different runs of the proposed method, a numerical string will be attached after the name "SP-CNN" to indicate different input neighborhood.…”

Section: Resultsmentioning

confidence: 99%

“…The CNN method, fig. 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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“…We have used data for the same area in our previous work [32], which represents sentinel 2 images of Fayoum Governorate, Egypt as shown in FIGURE 9, in addition to field trips data for the period from January to March 2016. Four bands of Sentinel satellite with 10 meters spatial resolution and six bands with 20 meters which were resampled to 10 meters, so we have 10 bands as a spectral signature for each pixel.…”

Section: A Study Area and Materialsmentioning

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

Cited by 9 publications

References 23 publications

Big Data and Machine Learning With Hyperspectral Information in Agriculture

Big Data and Machine Learning With Hyperspectral Information in Agriculture

Shallow Parallel CNNs for contextual remote sensing image classification

Sparse Pixel Training of Convolutional Neural Networks for Land Cover Classification

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