A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

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INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-1-w1-115-2017 ◽

2017 ◽

Vol IV-1/W1 ◽

pp. 115-123 ◽

Cited By ~ 7

Author(s):

R. Niessner ◽

H. Schilling ◽

B. Jutzi

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Vehicle Classification ◽

Support Vector ◽

Lidar Data ◽

Data Set

In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks. To study the potential of the Convolutional Neural Network, in this paper three approaches are investigated to train classifiers based on Convolutional Neural Networks. These approaches allow Convolutional Neural Networks to be trained on datasets containing only a few hundred training samples, which results in a successful classification. Two of these approaches are based on the concept of transfer learning. In the first approach features, created by a pretrained Convolutional Neural Network, are used for a classification using a support vector machine. In the second approach a pretrained Convolutional Neural Network gets fine-tuned on a different data set. The third approach includes the design and training for flat Convolutional Neural Networks from the scratch. The evaluation of the proposed approaches is based on a data set provided by the IEEE Geoscience and Remote Sensing Society (GRSS) which contains RGB and LiDAR data of an urban area. In this work it is shown that these Convolutional Neural Networks lead to classification results with high accuracy both on RGB and LiDAR data. Features which are derived by RGB data transferred into LiDAR data by transfer learning lead to better results in classification in contrast to RGB data. Using a neural network which contains fewer layers than common neural networks leads to the best classification results. In this framework, it can furthermore be shown that the practical application of LiDAR images results in a better data basis for classification of vehicles than the use of RGB images.

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Discrimination of Pepper Seed Varieties by Multispectral Imaging Combined with Machine Learning

Applied Engineering in Agriculture ◽

10.13031/aea.13794 ◽

2020 ◽

Vol 36 (5) ◽

pp. 743-749

Author(s):

Xingwang Li ◽

Xiaofei Fan ◽

Lili Zhao ◽

Sheng Huang ◽

Yi He ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Multispectral Imaging ◽

Classification Model ◽

One Dimensional ◽

Pepper Seed ◽

Better Than

HighlightsThis study revealed the feasibility of to classify pepper seed varieties using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN).Convolutional neural networks were adopted to develop models for prediction of seed varieties, and the performance was compared with KNN and SVM.In this experiment, the classification effect of the SVM classification model is the best, but the 1D-CNN classification model is relatively easy to implement.Abstract. When non-seed materials are mixed in seeds or seed varieties of low value are mixed in high value varieties, it will cause losses to growers or businesses. Thus, the successful discrimination of seed varieties is critical for improvement of seed ralue. In recent years, convolutional neural networks (CNNs) have been used in classification of seed varieties. The feasibility of using multispectral imaging combined with one-dimensional convolutional neural network (1D-CNN) to classify pepper seed varieties was studied. The total number of three varieties of samples was 1472, and the average spectral curve between 365nm and 970nm of the three varieties was studied. The data were analyzed using full bands of the spectrum or the feature bands selected by successive projection algorithm (SPA). SPA extracted 9 feature bands from 19 bands (430, 450, 470, 490, 515, 570, 660, 780, and 880 nm). The classification accuracy of the three classification models developed with full band using K nearest neighbors (KNN), support vector machine (SVM), and 1D-CNN were 85.81%, 97.70%, and 90.50%, respectively. With full bands, SVM and 1D-CNN performed significantly better than KNN, and SVM performed slightly better than 1D-CNN. With feature bands, the testing accuracies of SVM and 1D-CNN were 97.30% and 92.6%, respectively. Although the classification accuracy of 1D-CNN was not the highest, the ease of operation made it the most feasible method for pepper seed variety prediction. Keywords: Multispectral imaging, One-dimensional convolutional neural network, Pepper seed, Variety classification.

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Identifying Emotion on Indonesian Tweets using Convolutional Neural Networks

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i3.3137 ◽

2021 ◽

Vol 5 (3) ◽

pp. 584-593

Author(s):

Naufal Hilmiaji ◽

Kemas Muslim Lhaksmana ◽

Mahendra Dwifebri Purbolaksono

Keyword(s):

Neural Network ◽

Neural Networks ◽

Performance Evaluation ◽

Deep Learning ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Text Classification ◽

Classification Model ◽

Learning Methods ◽

Expected Performance

especially with the advancement of deep learning methods for text classification. Despite some effort to identify emotion on Indonesian tweets, its performance evaluation results have not achieved acceptable numbers. To solve this problem, this paper implements a classification model using a convolutional neural network (CNN), which has demonstrated expected performance in text classification. To easily compare with the previous research, this classification is performed on the same dataset, which consists of 4,403 tweets in Indonesian that were labeled using five different emotion classes: anger, fear, joy, love, and sadness. The performance evaluation results achieve the precision, recall, and F1-score at respectively 90.1%, 90.3%, and 90.2%, while the highest accuracy achieves 89.8%. These results outperform previous research that classifies the same classification on the same dataset.

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Classification of surface water objects in visible spectrum images

Journal of «Almaz – Antey» Air and Defence Corporation ◽

10.38013/2542-0542-2020-1-87-95 ◽

2020 ◽

pp. 87-95

Author(s):

A. A. Artemyev ◽

E. A. Kazachkov ◽

S. N. Matyugin ◽

V. V. Sharonov

Keyword(s):

Neural Network ◽

Neural Networks ◽

Surface Water ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Visible Spectrum ◽

Training Dataset ◽

And Training ◽

Selection Of

This paper considers the problem of classifying surface water objects, e.g. ships of different classes, in visible spectrum images using convolutional neural networks. A technique for forming a database of images of surface water objects and a special training dataset for creating a classification are presented. A method for forming and training of a convolutional neural network is described. The dependence of the probability of correct recognition on the number and variants of the selection of specific classes of surface water objects is analysed. The results of recognizing different sets of classes are presented.

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Deep convolutional neural networks for Raman spectrum recognition: a unified solution

The Analyst ◽

10.1039/c7an01371j ◽

2017 ◽

Vol 142 (21) ◽

pp. 4067-4074 ◽

Cited By ~ 81

Author(s):

Jinchao Liu ◽

Margarita Osadchy ◽

Lorna Ashton ◽

Michael Foster ◽

Christopher J. Solomon ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Raman Spectrum ◽

Convolutional Neural Network ◽

Raman Spectra ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks

Classification of unprocessed Raman spectra using a convolutional neural network.

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Classification of UAVs' distorted images using Convolutional Neural Networks

10.5753/wvc.2020.13488 ◽

2020 ◽

Author(s):

Leandro Silva ◽

Jocival D. Júnior ◽

Jean Santos ◽

João Fernando Mari ◽

Maurício Escarpinati ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Unmanned Aerial Vehicles ◽

Convolutional Neural Networks ◽

Precision Agriculture ◽

Aerial Vehicles ◽

Promising Area

Currently, the use of unmanned aerial vehicles (UAVs) is becoming ever more common for acquiring images in precision agriculture, either to identify characteristics of interest or to estimate plantations. However, despite this growth, their processing usually requires specialized techniques and software. During flight, UAVs may undergo some variations, such as wind interference and small altitude variations, which directly influence the captured images. In order to address this problem, we proposed a Convolutional Neural Network (CNN) architecture for the classification of three linear distortions common in UAV flight: rotation, translation and perspective transformations. To train and test our CNN, we used two mosaics that were divided into smaller individual images and then artificially distorted. Results demonstrate the potential of CNNs for solving possible distortions caused in the images during UAV flight. Therefore this becomes a promising area of exploration.

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Classification of tectonic and non-tectonic seismicity based on convolutional neural network

Geophysical Journal International ◽

10.1093/gji/ggaa444 ◽

2020 ◽

Vol 224 (1) ◽

pp. 191-198

Author(s):

Xinliang Liu ◽

Tao Ren ◽

Hongfeng Chen ◽

Yufeng Chen

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Proposed Model ◽

Single Station ◽

Using Data ◽

Fully Connected

SUMMARY In this paper, convolutional neural networks (CNNs) were used to distinguish between tectonic and non-tectonic seismicity. The proposed CNNs consisted of seven convolutional layers with small kernels and one fully connected layer, which only relied on the acoustic waveform without extracting features manually. For a single station, the accuracy of the model was 0.90, and the event accuracy could reach 0.93. The proposed model was tested using data from January 2019 to August 2019 in China. The event accuracy could reach 0.92, showing that the proposed model could distinguish between tectonic and non-tectonic seismicity.

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Golf swing classification with multiple deep convolutional neural networks

International Journal of Distributed Sensor Networks ◽

10.1177/1550147718802186 ◽

2018 ◽

Vol 14 (10) ◽

pp. 155014771880218 ◽

Cited By ~ 4

Author(s):

Libin Jiao ◽

Rongfang Bie ◽

Hao Wu ◽

Yu Wei ◽

Jixin Ma ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Golf Swing ◽

Support Vector ◽

Classification Methods ◽

Deep Convolutional Neural Networks ◽

Machine Method ◽

And Performance

The use of smart sports equipment and body sensory systems supervising daily sports training is gradually emerging in professional and amateur sports; however, the problem of processing large amounts of data from sensors used in sport and discovering constructive knowledge is a novel topic and the focus of our research. In this article, we investigate golf swing data classification methods based on varieties of representative convolutional neural networks (deep convolutional neural networks) which are fed with swing data from embedded multi-sensors, to group the multi-channel golf swing data labeled by hybrid categories from different golf players and swing shapes. In particular, four convolutional neural classifiers are customized: “GolfVanillaCNN” with the convolutional layers, “GolfVGG” with the stacked convolutional layers, “GolfInception” with the multi-scale convolutional layers, and “GolfResNet” with the residual learning. Testing on the real-world swing dataset sampled from the system integrating two strain gage sensors, three-axis accelerometer, and three-axis gyroscope, we explore the accuracy and performance of our convolutional neural network–based classifiers from two perspectives: classification implementations and sensor combinations. Besides, we further evaluate the performance of these four classifiers in terms of classification accuracy, precision–recall curves, and F1 scores. These common classification indicators illustrate that our convolutional neural network–based classifiers can basically group the golf swing predefined by the combination of shapes and golf players correctly and outperform support vector machine method representing traditional classification methods.

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Deep Convolutional Neural Networks for Tea Tree Pest Recognition and Diagnosis

Symmetry ◽

10.3390/sym13112140 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2140

Author(s):

Jing Chen ◽

Qi Liu ◽

Lingwang Gao

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Network ◽

Fine Tuning ◽

Support Vector ◽

Tea Tree ◽

Symmetry Properties ◽

Tea Pests

Due to the benefits of convolutional neural networks (CNNs) in image classification, they have been extensively used in the computerized classification and focus of crop pests. The intention of the current find out about is to advance a deep convolutional neural network to mechanically identify 14 species of tea pests that possess symmetry properties. (1) As there are not enough tea pests images in the network to train the deep convolutional neural network, we proposes to classify tea pests images by fine-tuning the VGGNET-16 deep convolutional neural network. (2) Through comparison with traditional machine learning algorithms Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), the performance of our method is evaluated (3) The three methods can identify tea tree pests well: the proposed convolutional neural network classification has accuracy up to 97.75%, while MLP and SVM have accuracies of 76.07% and 68.81%, respectively. Our proposed method performs the best of the assessed recognition algorithms. The experimental results also show that the fine-tuning method is a very powerful and efficient tool for small datasets in practical problems.

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Classification of Paintings Authorship Using Convolutional Neural Network

10.21528/cbic2021-116 ◽

2021 ◽

Author(s):

Richardson Santiago Teles Menezes ◽

Angelo Marcelino Cordeiro ◽

Rafael Magalhães ◽

Helton Maia

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

State Of The Art

In this paper, state-of-the-art architectures of Convolutional Neural Networks (CNNs) are explained and compared concerning authorship classification of famous paintings. The chosen CNNs architectures were VGG-16, VGG-19, Residual Neural Networks (ResNet), and Xception. The used dataset is available on the website Kaggle, under the title “Best Artworks of All Time”. Weighted classes for each artist with more than 200 paintings present in the dataset were created to represent and classify each artist’s style. The performed experiments resulted in an accuracy of up to 95% for the Xception architecture with an average F1-score of 0.87, 92% of accuracy with an average F1-score of 0.83 for the ResNet in its 50-layer configuration, while both of the VGG architectures did not present satisfactory results for the same amount of epochs, achieving at most 60% of accuracy.

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