scholarly journals Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

2021 ◽  
Vol 13 (15) ◽  
pp. 2965
Author(s):  
Saman Ghaffarian ◽  
Joao Valente ◽  
Mariska van der Voort ◽  
Bedir Tekinerdogan
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Literature Review ◽  
Change Detection ◽  
Object Detection ◽  
Image Classification ◽  
Systematic Literature Review ◽  
Attention Mechanism ◽  
Data Types

Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

scholarly journals CANet: A Combined Attention Network for Remote Sensing Image Change Detection

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 364
Author(s):  
Di Lu ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li ◽  
Anyu Du
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Change Detection ◽  
Remote Sensing Image ◽  
Attention Mechanism ◽  
Feature Maps ◽  
Qualitative Comparison ◽  
Remote Sensing Image Processing ◽  
Innovative Concept

Change detection (CD) is one of the essential tasks in remote sensing image processing and analysis. Remote sensing CD is a process of determining and evaluating changes in various surface objects over time. The impressive achievements of deep learning in image processing and computer vision provide an innovative concept for the task of CD. However, existing methods based on deep learning still have problems detecting small changed regions correctly and distinguishing the boundaries of the changed regions. To solve the above shortcomings and improve the efficiency of CD networks, inspired by the fact that an attention mechanism can refine features effectively, we propose an attention-based network for remote sensing CD, which has two important components: an asymmetric convolution block (ACB) and a combined attention mechanism. First, the proposed method extracts the features of bi-temporal images, which contain two parallel encoders with shared weights and structures. Then, the feature maps are fed into the combined attention module to reconstruct the change maps and obtain refined feature maps. The proposed CANet is evaluated on the two publicly available datasets for challenging remote sensing image CD. Extensive empirical results with four popular metrics show that the designed framework yields a robust CD detector with good generalization performance. In the CDD and LEVIR-CD datasets, the F1 values of the CANet are 3.3% and 1.3% higher than those of advanced CD methods, respectively. A quantitative analysis and qualitative comparison indicate that our method outperforms competitive baselines in terms of both effectiveness and robustness.

RNN-based multispectral satellite image processing for remote sensing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkata Dasu Marri ◽  
Veera Narayana Reddy P. ◽  
Chandra Mohan Reddy S.
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Image Classification ◽  
Satellite Image ◽  
Multispectral Image ◽  
Content Type ◽  
Colliding Bodies Optimization ◽  
Sensing Applications ◽  
Remote Sensing Applications

Purpose Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy. Design/methodology/approach This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image. Findings The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods. Originality/value In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

scholarly journals Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

2021 ◽  
Vol 11 (18) ◽  
pp. 8383 ◽  
Author(s):  
Muaadh A. Alsoufi ◽  
Shukor Razak ◽  
Maheyzah Md Siraj ◽  
Ibtehal Nafea ◽  
Fuad A. Ghaleb ◽  
...  
Keyword(s):  
Deep Learning ◽  
Literature Review ◽  
Intrusion Detection ◽  
Systematic Literature Review ◽  
Detection System ◽  
Smart Cities ◽  
Cyber Attacks ◽  
Data Types ◽  
Learning Techniques ◽  
Wide Range

The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus, Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction.

Digital Image Classification Techniques

2019 ◽  
pp. 162-187
Author(s):  
Utkarsh Shrivastav ◽  
Sanjay Kumar Singh
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Ct Scan ◽  
Image Classification ◽  
Digital Image ◽  
Digital Cameras ◽  
Learning Networks ◽  
Hidden Information ◽  
Classification Techniques

Image classification is a technique to categorize an image in to given classes on the basis of hidden characteristics or features extracted using image processing. With rapidly growing technology, the size of images is growing. Different categories of images may contain different types of hidden information such as x-ray, CT scan, MRI, pathologies images, remote sensing images, satellite images, and natural scene image captured via digital cameras. In this chapter, the authors have surveyed various articles and books and summarized image classification techniques. There are supervised techniques like KNN and SVM, which classify an image into given classes and unsupervised techniques like K-means and ISODATA for classifying image into a group of clusters. For big images, deep learning networks can be employed that are fast and efficient and also compute hidden features automatically.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

EEG signal analysis using deep learning: A systematic literature review

2021 ◽  
Author(s):  
Ghita Amrani ◽  
Amina Adadi ◽  
Mohammed Berrada ◽  
Zouhayr Souirti ◽  
Said Boujraf
Keyword(s):  
Deep Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Eeg Signal Analysis

Deep Learning in Smart Video Surveillance for Crowd Management: A Systematic Literature Review

2021 ◽  
Author(s):  
Andrea Camille Garcia ◽  
Jealine Eleanor Gorre ◽  
Joshua Angelo Karl Perez ◽  
Mary Jane Samonte
Keyword(s):  
Deep Learning ◽  
Literature Review ◽  
Video Surveillance ◽  
Systematic Literature Review ◽  
Crowd Management
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