scholarly journals Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

2019 ◽  
Vol 11 (17) ◽  
pp. 1986 ◽  
Author(s):  
Andrei Stoian ◽  
Vincent Poulain ◽  
Jordi Inglada ◽  
Victor Poughon ◽  
Dawa Derksen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
Operational Systems ◽  
Land Cover Maps ◽  
Sentinel 2

The Sentinel-2 satellite mission offers high resolution multispectral time-series image data, enabling the production of detailed land cover maps globally. When mapping large territories, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixel-wise classification methods. However, the radical shift of the computer vision field away from hand-engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In particular, convolutional neural networks learn features which take into account the context of the pixels and, therefore, a better representation of the data can be obtained. In this paper, we assess fully convolutional neural network architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time-series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time-series image data, an adaptation of the U-Net model (a fully convolutional neural network) for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps. We conclude that fully convolutional neural networks can yield improved results with respect to pixel-wise Random Forest classifiers for classes where texture and context are pertinent. However, this new approach shows higher variability in quality across different landscapes and comes with a computational cost which could be to high for operational systems.

scholarly journals Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

2019 ◽  
Author(s):  
Andrei Stoian ◽  
Vincent Poulain ◽  
Jordi Inglada ◽  
Victor Poughon ◽  
Dawa Derksen
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Satellite Image ◽  
Image Data ◽  
Design Decision ◽  
Land Cover Maps ◽  
Sentinel 2

The Sentinel-2 satellite mission offers high resolution multispectral time series image data, enabling the production of detailed land cover maps globally. At this scale, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixelwise classification methods. The radical shift of the computer vision field away from hand engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In this paper we assess fully convolutional neural networks architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time series image data, an adaptation of the U-Net model for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps.

scholarly journals Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems

2019 ◽  
Author(s):  
Andrei Stoian ◽  
Vincent Poulain ◽  
Jordi Inglada ◽  
Victor Poughon ◽  
Dawa Derksen
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Satellite Image ◽  
Image Data ◽  
Design Decision ◽  
Land Cover Maps ◽  
Sentinel 2

The Sentinel-2 satellite mission offers high resolution multispectral time series image data, enabling the production of detailed land cover maps globally. At this scale, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixelwise classification methods. The radical shift of the computer vision field away from hand engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In this paper we assess fully convolutional neural networks architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time series image data, an adaptation of the U-Net model for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps.

Deep convolutional neural networks for land-cover classification with Sentinel-2 images

2019 ◽  
Vol 13 (02) ◽  
pp. 1 ◽  
Author(s):  
Eleni Kroupi ◽  
Maria Kesa ◽  
Victor Diego Navarro-Sánchez ◽  
Salman Saeed ◽  
Camille Pelloquin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Land Cover Classification ◽  
Deep Convolutional Neural Networks ◽  
Sentinel 2

scholarly journals A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1960 ◽  
Author(s):  
Lu Han ◽  
Chongchong Yu ◽  
Kaitai Xiao ◽  
Xia Zhao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Classification ◽  
Mixed Gas ◽  
Gas Identification ◽  
Mixed Gases

This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks—VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50—were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.

scholarly journals Tinjauan Literatur Sistematik tentang Deteksi Covid-19 menggunakan Convolutional Neural Networks

2021 ◽  
Vol 8 (1) ◽  
pp. 9
Author(s):  
Buyut Khoirul Umri ◽  
Ema Utami ◽  
Mei P Kurniawan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Literature Review ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Image Data ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray

Covid-19 menyerang sel-sel epitel yang melapisi saluran pernapasan sehingga dalam kasus ini dapat memanfaatkan gambar x-ray dada untuk menganalisis kesehatan paru-paru pada pasien. Menggunakan x-ray dalam bidang medis merupakan metode yang lebih cepat, lebih mudah dan tidak berbahaya yang dapat dimanfaatkan pada banyak hal. Salah satu metode yang paling sering digunakan dalam klasifikasi gambar adalah convolutional neural networks (CNN). CNN merupahan jenis neural network yang sering digunakan dalam data gambar dan sering digunakan dalam mendeteksi dan mengenali object pada sebuah gambar. Model arsitektur pada metode CNN juga dapat dikembangkan dengan transfer learning yang merupakan proses menggunakan kembali model pre-trained yang dilatih pada dataset besar, biasanya pada tugas klasifikasi gambar berskala besar. Tinjauan literature review ini digunakan untuk menganalisis penggunaan transfer learning pada CNN sebagai metode yang dapat digunakan untuk mendeteksi covid-19 pada gambar x-ray dada. Hasil sistematis review menunjukkan bahwa algoritma CNN dapat digunakan dengan akruasi yang baik dalam mendeteksi covid-19 pada gambar x-ray dada dan dengan pengembangan model transfer learning mampu mendapatkan performa yang maksimal dengan dataset yang besar maupun kecil.Kata Kunci—CNN, transfer learning, deteksi, covid-19Covid-19 attacks the epithelial cells lining the respiratory tract so that in this case it can utilize chest x-ray images to analyze the health of the lungs in patients. Using x-rays in the medical field is a faster, easier and harmless method that can be utilized in many ways. One of the most frequently used methods in image classification is convolutional neural networks (CNN). CNN is a type of neural network that is often used in image data and is often used in detecting and recognizing objects in an image. The architectural model in the CNN method can also be developed with transfer learning which is the process of reusing pre-trained models that are trained on large datasets, usually on the task of classifying large-scale images. This literature review review is used to analyze the use of transfer learning on CNN as a method that can be used to detect covid-19 on chest x-ray images. The systematic review results show that the CNN algorithm can be used with good accuracy in detecting covid-19 on chest x-ray images and by developing transfer learning models able to get maximum performance with large and small datasets.Keywords—CNN, transfer learning, detection, covid-19

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