A review of Convolutional Neural Networks in Remote Sensing Image

Liu, Xinni; Han, Fang; Ghazali, Kamarul Hawari; Mohamed, Izzeldin Ibrahim; Zhao, Yue

doi:10.1145/3316615.3316712

Cited by 19 publications

(10 citation statements)

References 21 publications

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“…AlexNet with 60 million parameters has eight layers, five convolutional layers and three fully connected layers. Every convolutional and fully connected layer used non-saturated ReLU gives the training response over tanh and sigmoid is improved [132]. Figure 11 shows the architecture of the AlexNet convolutional network that was proposed by Patino et al [133] in classification of tropical fruits with 2633 images of fruits divided into 15 categories, including high variability and complexity.…”

Section: Alexnetmentioning

confidence: 99%

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

et al. 2022

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The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.

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Section: Alexnetmentioning

confidence: 99%

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

et al. 2022

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“…Through the application of an ANN, GIS professionals can add another dimension to their spatial capabilities. In some research, a combination of neural networks with remote sensing image data for mapping the urbanization dynamics, has been proposed [20][21][22].…”

Section: Application Of Machine Learning In Gismentioning

confidence: 99%

A Review of the Machine Learning in GIS for Megacities Application

Tohidi¹,

Rustamov²

2020

Geographic Information Systems in Geospatial Intelligence

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Machine learning (ML) is very useful for analyzing data in many domains, including the satellite images processing. In the remote sensing data processing, ML tools are mainly founded out a place for filtering, interpretation and prediction information. Filtering aims at removing noise and performing transformations, which is vital segment of data processing as useful performance of data validation. An interpretation is significant part of it as the stage of objects classification depends of existing task for solution. Prediction is performed to estimate precise values of underlying parameters or future events in the data. It can be used successfully above achievements in a variety of areas. An urbanization is one of the spheres of advance technology application where highly need to collect appropriate data for understanding of challenges facing society. The process of urbanization becomes very important problem, thanks to city expansion. Each city is a complicated system. It consists of various interactive sub-systems and is affected by multiple factors, including population growth, transportation and management policies. To understand the driving forces of the urban structure change, the satellite-based estimates are considered to monitor these changes, in long term. GIS (geographic information system) is equivalent to methods related to the use of geospatial information. Besides, the increasing application of ML techniques in various fields, including GIS, is undeniable. Thus, the chapter attempts to review the application of ML techniques in GIS with a focus on megacities and theirs features fixing/identification and solution.

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“…According to these data, a model is fitted, and then the generated model is used to classify the sensor data of untested people [3]. Thanks to the research and development of convolutional neural network [4], these models can automatically perform feature learning on the original sensor data [5] and can fit a good model on the manually extracted domain-specific specific features and achieve excellent results in human activity recognition [6][7][8][9]. However, the data obtained from WiFi and RFID is 2D data and lacks accurate spatial information [10][11][12][13][14], so it is difficult to identify in some extreme environments.…”

Section: Introductionmentioning

confidence: 99%

mmBehavior: Human Activity Recognition System of millimeter-wave Radar Point Clouds Based on Deep Recurrent Neural Network

et al. 2023

Preprint

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Sensing human activities through wireless technology is the key to human-computer interaction. Relevant studies show that millimeter-wave radar has attracted more and more attention because it can accurately measure distance, velocity, and angle, and extract 3D point cloud images. However, the millimeter-wave point cloud is very sparse. For example, the depth neural network of PointNet is designed to deal with the LIDAR point cloud, which is not effective on the millimeter-wave radar point cloud. Therefore, in this paper, we propose a user activity perception system mmBheavior based on commercial millimeter-wave radar chip. Firstly, we use an improved clustering algorithm P-DBSCAN, which overcomes the problem of DBSCAN parameter sensitivity and realizes adaptive user target detection in the environment of changing number of people. Then we propose a new neural network architecture P-BiLSTMNet, which improves PointNet + + and introduces BiLSTM to learn the time dependence of point cloud. Experiments show that the system can classify all kinds of motion, and the accuracy is more than 93%.

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A review of Convolutional Neural Networks in Remote Sensing Image

Cited by 19 publications

References 21 publications

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

A Review of the Machine Learning in GIS for Megacities Application

mmBehavior: Human Activity Recognition System of millimeter-wave Radar Point Clouds Based on Deep Recurrent Neural Network

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