scholarly journals A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets

2020 ◽  
Vol 12 (12) ◽  
pp. 2010 ◽  
Author(s):  
Seyd Teymoor Seydi ◽  
Mahdi Hasanlou ◽  
Meisam Amani
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
State Of The Art ◽  
Polarimetric Synthetic Aperture Radar ◽  
Different Types ◽  
Benchmark Datasets ◽  
Convolution Kernels ◽  
2D And 3D ◽  
Accuracy Indices ◽  
Better Than

The diversity of change detection (CD) methods and the limitations in generalizing these techniques using different types of remote sensing datasets over various study areas have been a challenge for CD applications. Additionally, most CD methods have been implemented in two intensive and time-consuming steps: (a) predicting change areas, and (b) decision on predicted areas. In this study, a novel CD framework based on the convolutional neural network (CNN) is proposed to not only address the aforementioned problems but also to considerably improve the level of accuracy. The proposed CNN-based CD network contains three parallel channels: the first and second channels, respectively, extract deep features on the original first- and second-time imagery and the third channel focuses on the extraction of change deep features based on differencing and staking deep features. Additionally, each channel includes three types of convolution kernels: 1D-, 2D-, and 3D-dilated-convolution. The effectiveness and reliability of the proposed CD method are evaluated using three different types of remote sensing benchmark datasets (i.e., multispectral, hyperspectral, and Polarimetric Synthetic Aperture RADAR (PolSAR)). The results of the CD maps are also evaluated both visually and statistically by calculating nine different accuracy indices. Moreover, the results of the CD using the proposed method are compared to those of several state-of-the-art CD algorithms. All the results prove that the proposed method outperforms the other remote sensing CD techniques. For instance, considering different scenarios, the Overall Accuracies (OAs) and Kappa Coefficients (KCs) of the proposed CD method are better than 95.89% and 0.805, respectively, and the Miss Detection (MD) and the False Alarm (FA) rates are lower than 12% and 3%, respectively.

scholarly journals Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 261-273
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Network Architecture ◽  
State Of The Art ◽  
Ensemble Classification ◽  
Effective Vaccine ◽  
Rt Pcr ◽  
Neural Network Architecture ◽  
Experimental Phase ◽  
Different Types ◽  
Polymerase Chain ◽  
Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals A KERNEL-BASED SIMILARITY MEASURING FOR CHANGE DETECTION IN REMOTE SENSING IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 999-1006
Author(s):  
Xiaodan Shi ◽  
Guorui Ma ◽  
Fenge Chen ◽  
Yanli Ma
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Comparison Method ◽  
Kernel Functions ◽  
Multidimensional Data ◽  
Remote Sensing Images ◽  
Model Free ◽  
Change Vector ◽  
Similarity Measuring ◽  
Better Than

This paper presents a kernel-based approach for the change detection of remote sensing images. It detects change by comparing the probability density (PD), expressed as kernel functions, of the feature vector extracted from bi- temporal images. PD is compared by defined kernel functions without immediate PD estimation. This algorithm is model-free and it can process multidimensional data, and is fit for the images with rich texture in particular. Experimental results show that overall accuracy of the algorithm is 98.9 %, a little bit better than that of the change vector analysis and classification comparison method, which is 96.7 % and 95.9 % respectively.

scholarly journals Unsupervised Outlier Detection in Multidimensional Data

2021 ◽  
Author(s):  
Atiq Rehman ◽  
Samir Brahim Belhaouari
Keyword(s):  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Multidimensional Data ◽  
High Dimensions ◽  
Comprehensive Performance ◽  
Benchmark Datasets ◽  
Distance Vector ◽  
Detection Schemes ◽  
Unsupervised Outlier Detection ◽  
Better Than

Abstract Detection and removal of outliers in a dataset is a fundamental preprocessing task without which the analysis of the data can be misleading. Furthermore, the existence of anomalies in the data can heavily degrade the performance of machine learning algorithms. In order to detect the anomalies in a dataset in an unsupervised manner, some novel statistical techniques are proposed in this paper. The proposed techniques are based on statistical methods considering data compactness and other properties. The newly proposed ideas are found efficient in terms of performance, ease of implementation, and computational complexity. Furthermore, two proposed techniques presented in this paper use only a single dimensional distance vector to detect the outliers, so irrespective of the data’s high dimensions, the techniques remain computationally inexpensive and feasible. Comprehensive performance analysis of the proposed anomaly detection schemes is presented in the paper, and the newly proposed schemes are found better than the state-of-the-art methods when tested on several benchmark datasets.

scholarly journals Effective Representing of Information Network by Variational Autoencoder

2017 ◽  
Author(s):  
Hang Li ◽  
Haozheng Wang ◽  
Zhenglu Yang ◽  
Haochen Liu
Keyword(s):  
State Of The Art ◽  
Vector Model ◽  
Information Network ◽  
Network Representation ◽  
Linear Relationships ◽  
Variational Autoencoder ◽  
Benchmark Datasets ◽  
Text Information ◽  
Complex Features ◽  
Better Than

Network representation is the basis of many applications and of extensive interest in various fields, such as information retrieval, social network analysis, and recommendation systems. Most previous methods for network representation only consider the incomplete aspects of a problem, including link structure, node information, and partial integration. The present study proposes a deep network representation model that seamlessly integrates the text information and structure of a network. Our model captures highly non-linear relationships between nodes and complex features of a network by exploiting the variational autoencoder (VAE), which is a deep unsupervised generation algorithm. We also merge the representation learned with a paragraph vector model and that learned with the VAE to obtain the network representation that preserves both structure and text information. We conduct comprehensive empirical experiments on benchmark datasets and find our model performs better than state-of-the-art techniques by a large margin.

scholarly journals Category Trees - Classifiers that Branch on Category

2021 ◽  
Vol 12 (06) ◽  
pp. 65-76
Author(s):  
Kieran Greer
Keyword(s):  
State Of The Art ◽  
The State ◽  
Biological Analogy ◽  
Category Type ◽  
Benchmark Datasets ◽  
Batch Input ◽  
Distinguishing Features ◽  
Incorrect Data ◽  
Better Than

This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change therefore is to allow the classifier to branch to new layers when there is a split in the data, and create new classifiers there for the data rows that are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests a further innovation, which is to represent some data columns with fixed value ranges, or bands. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest must be classified using a classifier technique and the idea allows the paper to discuss a biological analogy with neurons and neuron links. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art.

scholarly journals PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection

2020 ◽  
Vol 12 (3) ◽  
pp. 484 ◽  
Author(s):  
Huiwei Jiang ◽  
Xiangyun Hu ◽  
Kun Li ◽  
Jinming Zhang ◽  
Jinqi Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Additional Data ◽  
State Of The Art ◽  
The Core ◽  
Siamese Network ◽  
Input Feature ◽  
Feature Based ◽  
Attention Level ◽  
Remarkable Progress

In recent years, building change detection has made remarkable progress through using deep learning. The core problems of this technique are the need for additional data (e.g., Lidar or semantic labels) and the difficulty in extracting sufficient features. In this paper, we propose an end-to-end network, called the pyramid feature-based attention-guided Siamese network (PGA-SiamNet), to solve these problems. The network is trained to capture possible changes using a convolutional neural network in a pyramid. It emphasizes the importance of correlation among the input feature pairs by introducing a global co-attention mechanism. Furthermore, we effectively improved the long-range dependencies of the features by utilizing various attention mechanisms and then aggregating the features of the low-level and co-attention level; this helps to obtain richer object information. Finally, we evaluated our method with a publicly available dataset (WHU) building dataset and a new dataset (EV-CD) building dataset. The experiments demonstrate that the proposed method is effective for building change detection and outperforms the existing state-of-the-art methods on high-resolution remote sensing orthoimages in various metrics.

scholarly journals Rethinking the Fourier-Mellin Transform: Multiple Depths in the Camera’s View

2021 ◽  
Vol 13 (5) ◽  
pp. 1000
Author(s):  
Qingwen Xu ◽  
Haofei Kuang ◽  
Laurent Kneip ◽  
Sören Schwertfeger
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Feature Detection ◽  
Mellin Transform ◽  
State Of The Art ◽  
Visual Odometry ◽  
Superior Performance ◽  
Fourier Mellin Transform ◽  
And Robotics ◽  
Better Than

Remote sensing and robotics often rely on visual odometry (VO) for localization. Many standard approaches for VO use feature detection. However, these methods will meet challenges if the environments are feature-deprived or highly repetitive. Fourier-Mellin Transform (FMT) is an alternative VO approach that has been shown to show superior performance in these scenarios and is often used in remote sensing. One limitation of FMT is that it requires an environment that is equidistant to the camera, i.e., single-depth. To extend the applications of FMT to multi-depth environments, this paper presents the extended Fourier-Mellin Transform (eFMT), which maintains the advantages of FMT with respect to feature-deprived scenarios. To show the robustness and accuracy of eFMT, we implement an eFMT-based visual odometry framework and test it in toy examples and a large-scale drone dataset. All these experiments are performed on data collected in challenging scenarios, such as, trees, wooden boards and featureless roofs. The results show that eFMT performs better than FMT in the multi-depth settings. Moreover, eFMT also outperforms state-of-the-art VO algorithms, such as ORB-SLAM3, SVO and DSO, in our experiments.

scholarly journals Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

2021 ◽  
Author(s):  
RG Negri ◽  
Alejandro Frery
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Pattern Analysis ◽  
Detection Method ◽  
Simulated Data ◽  
Detection Methods ◽  
Practical Significance ◽  
Significance Level ◽  
Natural Factors ◽  
Better Than

© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature. The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.

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