Possibilities of deep learning neural networks for satellite image recognition

Latest remote sensing sensors are capable of acquiring high spatial and spectral Satellite Image Time Series (SITS) of the world. These image series are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth’s surfaces. More specifically, current SITS combine high temporal, spectral and spatial resolutions, which makes it possible to closely monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied to create land cover maps from SITS, these algorithms do not make the most of the temporal domain. This paper proposes a comprehensive study of Temporal Convolutional Neural Networks (TempCNNs), a deep learning approach which applies convolutions in the temporal dimension in order to automatically learn temporal (and spectral) features. The goal of this paper is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification, as compared to RF and Recurrent Neural Networks (RNNs) —a standard deep learning approach that is particularly suited to temporal data. We carry out experiments on Formosat-2 scene with 46 images and one million labelled time series. The experimental results show that TempCNNs are more accurate than the current state of the art for SITS classification. We provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size; we also draw out some differences with standard results in computer vision (e.g., about pooling layers). Finally, we assess the visual quality of the land cover maps produced by TempCNNs.

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Deep Learning Based Image Recognition for 5G Smart IoT Applications

10.21203/rs.3.rs-108738/v1 ◽

2020 ◽

Author(s):

Jing Li ◽

Xinfang li ◽

Yuwen Ning

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Image Recognition ◽

Principal Component ◽

Recognition System ◽

Image Features ◽

Image Feature ◽

Support Vector ◽

Recognition Method ◽

Linear Discriminant

Abstract With the advent of the 5G era，the development of massive data learning algorithms and in-depth research on neural networks, deep learning methods are widely used in image recognition tasks. However, there is currently a lack of methods for identifying and classifying efficiently Internet of Things (IoT) images. This paper develops an IoT image recognition system based on deep learning, i.e., uses convolutional neural networks (CNN) to construct image recognition algorithms, and uses principal component analysis (PCA) and linear discriminant analysis (LDA) to extract image features, respectively. The effectiveness of the two PCA and LDA image recognition methods is verified through experiments. And when the image feature dimension is 25, the best image recognition effect can be obtained. The main classifier used for image recognition in the IoT is the support vector machine (SVM), and the SVM and CNN are trained by using the database of this paper. At the same time, the effectiveness of the two for image recognition is checked, and then the trained classifier is used for image recognition. It is found that a CNN and SVM-based secondary classification IoT image recognition method improves the accuracy of image recognition. The secondary classification method combines the characteristics of the SVM and CNN image recognition methods, and the accuracy of the image recognition method is verified to provide an effective improvement through experimental verification.

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Remaining Useful Life Estimation Using Neural Ordinary Differential Equations

International Journal of Prognostics and Health Management ◽

10.36001/ijphm.2021.v12i2.2938 ◽

2021 ◽

Vol 12 (2) ◽

Author(s):

Marco Star ◽

Kristoffer McKee

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Differential Equations ◽

Ordinary Differential Equations ◽

Image Recognition ◽

State Of The Art ◽

Recognition Task ◽

Remaining Useful Life ◽

Learning Methods ◽

Time Frequency

Data-driven machinery prognostics has seen increasing popularity recently, especially with the effectiveness of deep learning methods growing. However, deep learning methods lack useful properties such as the lack of uncertainty quantification of their outputs and have a black-box nature. Neural ordinary differential equations (NODEs) use neural networks to define differential equations that propagate data from the inputs to the outputs. They can be seen as a continuous generalization of a popular network architecture used for image recognition known as the Residual Network (ResNet). This paper compares the performance of each network for machinery prognostics tasks to show the validity of Neural ODEs in machinery prognostics. The comparison is done using NASA’s Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, which simulates the sensor information of degrading turbofan engines. To compare both architectures, they are set up as convolutional neural networks and the sensors are transformed to the time-frequency domain through the short-time Fourier transform (STFT). The spectrograms from the STFT are the input images to the networks and the output is the estimated RUL; hence, the task is turned into an image recognition task. The results found NODEs can compete with state-of-the-art machinery prognostics methods. While it does not beat the state-of-the-art method, it is close enough that it could warrant further research into using NODEs. The potential benefits of using NODEs instead of other network architectures are also discussed in this work.

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Experimental Discussion on Fire Image Recognition Based on Deep Learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/2066/1/012071 ◽

2021 ◽

Vol 2066 (1) ◽

pp. 012071

Author(s):

Yongyi Cui ◽

Fang Qu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Image Recognition ◽

Rapid Development ◽

Fire Detection ◽

Learning Technology ◽

Fire Recognition

Abstract Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

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Adversarial Attacks for Deep Learning-Based Infrared Object Detection

Journal of the Korea Institute of Military Science and Technology ◽

10.9766/kimst.2021.24.6.591 ◽

2021 ◽

Vol 24 (6) ◽

pp. 591-601

Author(s):

Hoseong Kim ◽

Jaeguk Hyun ◽

Hyunjung Yoo ◽

Chunho Kim ◽

Hyunho Jeon

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Object Detection ◽

Image Recognition ◽

Rapid Growth ◽

Deep Neural Networks ◽

State Of The Art ◽

Visible Image ◽

Adversarial Attack

Recently, infrared object detection(IOD) has been extensively studied due to the rapid growth of deep neural networks(DNN). Adversarial attacks using imperceptible perturbation can dramatically deteriorate the performance of DNN. However, most adversarial attack works are focused on visible image recognition(VIR), and there are few methods for IOD. We propose deep learning-based adversarial attacks for IOD by expanding several state-of-the-art adversarial attacks for VIR. We effectively validate our claim through comprehensive experiments on two challenging IOD datasets, including FLIR and MSOD.

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A Dynamic Multi Label Image Classification based on Recurrent Neural Networks

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9817.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 5093-5096

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Image Classification ◽

Image Recognition ◽

Recurrent Neural Networks ◽

Sequential Analysis ◽

Traditional Technique ◽

The Future ◽

The Matrix ◽

Computation Speed

The traditional technique used for image recognition has complexity in the construction of algorithm and the training speed for the system to analyze algorithm is also too high so, the computation of the algorithm becomes very difficult in order to overcome this lack of computation. The proposed system is very efficient in both training as well as the computation speed required for the image recognition. Since, the proposed system uses the traditional LSTM algorithm which is one of the backbone factors of the RNN technique as it predicts the input on the basis of sequential analysis as it uses tanh function in order to remove the negative values of the matrix and it also predicts and removes the error in the input with the use of differential formulas in order to formulate the outcome desired for the image to be recognized. Because of this sequential analysis of the data increases the future scope of image recognition in the field of deep learning, and also because of its efficient use of the algorithm in comparison with the existing algorithm like ANN, CNN.

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Deep Learning Techniques for Agronomy Applications

Agronomy ◽

10.3390/agronomy9030142 ◽

2019 ◽

Vol 9 (3) ◽

pp. 142 ◽

Cited By ~ 6

Author(s):

Chi-Hua Chen ◽

Hsu-Yang Kung ◽

Feng-Jang Hwang

Keyword(s):

Neural Networks ◽

Time Series ◽

Deep Learning ◽

Data Analysis ◽

Image Recognition ◽

Time Series Data ◽

Series Data ◽

Strategy Analysis ◽

Analysis Techniques ◽

Time Series Data Analysis

This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) “Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks,” by Chen et al.; (2) “Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques,” by Alvarez et al.; and (3) “Development of a mushroom growth measurement system applying deep learning for image recognition,” by Lu et al. One paper on DL-based time series data analysis techniques for agronomy applications is as follows: “LSTM neural network based forecasting model for wheat production in Pakistan,” by Haider et al. One paper on behavior and strategy analysis for agronomy applications is as follows: “Research into the E-learning model of agriculture technology companies: analysis by deep learning,” by Lin et al.

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Smart Vision Based ATM Transaction using Deep Learning Neural Networks

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9075.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 5126-5132

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Deep Learning ◽

Image Recognition ◽

Character Recognition ◽

Optical Character Recognition ◽

Credit Cards ◽

Optical Character ◽

Online Payment ◽

Visually Challenged

The necessity of credit cards and online payment techniques become extremely popular and simple to perform because of easy and safe money handling techniques. The usage of the ATM by visually challenged people is a problem. Though there are certain features for the visually challenged users like speech instructions, there is no conformity of the amount entered or of that transacted. As a result, these people have no security, ease or comfort during the ATM transactions. So, there is a need to provide a method for the visually challenged people to effortlessly perform ATM transactions with better security. Our proposed system designed a device that can act as an aid for the visually challenged to transact in the ATM. The devised system recognizes the amount to be transacted as entered on the screen using Optical Character Recognition (OCR) and conveys it to the user via speech. After transaction, the banknotes are recognized by the system using image recognition through vital banknote feature extraction and the verification is provided regarding the amount transacted and the intended amount.

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Extracting the height of lettuce by using neural networks of image recognition in deep learning

10.1002/essoar.10508411.1 ◽

2021 ◽

Author(s):

Minjuan Wang ◽

Xiyue Guo ◽

Yong Zhong ◽

Yarong Feng ◽

Ming Zhao ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Image Recognition

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A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Sensors ◽

10.3390/s21144749 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4749

Author(s):

Vijaypal Singh Dhaka ◽

Sangeeta Vaibhav Meena ◽

Geeta Rani ◽

Deepak Sinwar ◽

Kavita Kavita ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Image Recognition ◽

Optimization Techniques ◽

Plant Diseases ◽

Deep Convolutional Neural Networks ◽

Plant Leaf ◽

Advantages And Disadvantages ◽

Learning Techniques

In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

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