scholarly journals Unsupervised Clustering of Neighborhood Associations and Image Segmentation Applications

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 309
Author(s):  
Zhenggang Wang ◽  
Xuantong Li ◽  
Jin Jin ◽  
Zhong Liu ◽  
Wei Liu
Keyword(s):  
Remote Sensing ◽  
Clustering Analysis ◽  
Clustering Algorithms ◽  
Optimal Solution ◽  
Remote Sensing Image ◽  
Neighborhood Density ◽  
Correlation Clustering ◽  
Density Correlation ◽  
Advantages And Disadvantages ◽  
Data Points

Irregular shape clustering is always a difficult problem in clustering analysis. In this paper, by analyzing the advantages and disadvantages of existing clustering analysis algorithms, a new neighborhood density correlation clustering (NDCC) algorithm for quickly discovering arbitrary shaped clusters. Because the density of the center region of any cluster sample dataset is greater than that of the edge region, the data points can be divided into core, edge, and noise data points, and then the density correlation of the core data points in their neighborhood can be used to form a cluster. Further more, by constructing an objective function and optimizing the parameters automatically, a locally optimal result that is close to the globally optimal solution can be obtained. This algorithm avoids the clustering errors caused by iso-density points between clusters. We compare this algorithm with other five clustering algorithms and verify it on two common remote sensing image datasets. The results show that it can cluster the same ground objects in remote sensing images into one class and distinguish different ground objects. NDCC has strong robustness to irregular scattering dataset and can solve the clustering problem of remote sensing image.

scholarly journals A Study of Image Classification of Remote Sensing Based on Back-Propagation Neural Network with Extended Delta Bar Delta

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Shi Liang Zhang ◽  
Ting Cheng Chang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Classification ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Geographic Information ◽  
Classification Algorithm ◽  
Classification Model ◽  
Landsat 8 ◽  
Advantages And Disadvantages

This paper proposes a model to extract feature information quickly and accurately identifying what cannot be achieved through traditional methods of remote sensing image classification. First, process the selected Landsat-8 remote sensing data, including radiometric calibration, geometric correction, optimal band combination, and image cropping. Add the processed remote sensing image to the normalized geographic auxiliary information, digital elevation model (DEM), and normalized difference vegetation index (NDVI), working together to build a neural network that consists of three levels based on the structure of back-propagation neural and extended delta bar delta (BPN-EDBD) algorithm, determining the parameters of the neural network to constitute a good classification model. Then determine classification and standards via field surveys and related geographic information; select training samples BPN-EDBD for algorithm learning and training and, if necessary, revise and improve its parameters using the BPN-EDBD classification algorithm to classify the remote sensing image after pretreatment and DEM data and NDVI as input parameters and output classification results, and run accuracy assessment. Finally, compare with traditional supervised classification algorithms, while adding different auxiliary geographic information to compare classification results to study the advantages and disadvantages of BPN-EDBD classification algorithm.

scholarly journals Comparative Analysis of Clustering Techniques for Movie Recommendation

2018 ◽  
Vol 225 ◽  
pp. 02004
Author(s):  
T.S. Aditya ◽  
Karthik Rajaraman ◽  
M. Monica Subashini
Keyword(s):  
Neural Network ◽  
Clustering Algorithms ◽  
Mean Shift ◽  
Graphics Processors ◽  
Clustering Techniques ◽  
Advantages And Disadvantages ◽  
Mean Shift Clustering ◽  
Data Points ◽  
Energy Consuming ◽  
Movie Recommendation

Movie recommendation is a subject with immense ambiguity. A person might like a movie but not a very similar movie. The present recommending systems focus more on just few parameters such as Director, cast and genre. A lot of Power intensive methods such as Deep Convolutional Neural Network (CNN) has been used which demands the use of Graphics processors that require more energy. We try to accomplish the same task using lesser Energy consuming algorithms such as clustering techniques. In this paper, we try to create a more generalized list of similar movies in order to provide the user with more variety of movies which he/she might like, using clustering algorithms. We will compare how choosing different parameters and number of features affect the cluster's content. Also, compare how different algorithms such as K-mean, Hierarchical, Birch and mean shift clustering algorithms give a varied result and conclude which method will suit for which scenarios of movie recommendations. We also conclude on which algorithm clusters stray data points more efficiently and how different algorithms provide different advantages and disadvantages.

Data Mining Clustering Algorithm Research and Application

2014 ◽  
Vol 926-930 ◽  
pp. 3608-3611 ◽  
Author(s):  
Yi Fan Zhang ◽  
Yong Tao Qian ◽  
Tai Yu Liu ◽  
Shu Yan Wu
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Dimensional Space ◽  
Data Mining Algorithm ◽  
Practical Application ◽  
Advantages And Disadvantages ◽  
Analysis Theory ◽  
Data Points

In this paper, first introduce data mining knowledge then focuses on the clustering analysis algorithms, including classification clustering algorithm, and each classification typical cluster analysis algorithms, including the formal description of each algorithm as well as the advantages and disadvantages of each algorithm also has a more detailed description. Then carefully introduce data mining algorithm on the basis of cluster analysis. And using cohesion based clustering algorithm with DBSCAN algorithm and clustering in consumer spending in two-dimensional space, 2,000 data points for each area, and get a reasonable clustering results, resulting in hierarchical clustering results valuable information, so as to realize the practical application of the algorithm and clustering analysis theory combined.

Remote sensing image landmark segmentation algorithm based on improved GSA and PCNN combination

2020 ◽  
pp. 002072092093682
Author(s):  
Zilong Liu ◽  
Guobin Chen
Keyword(s):  
Remote Sensing ◽  
Optimal Solution ◽  
Entropy Change ◽  
Remote Sensing Image ◽  
Segmentation Algorithm ◽  
Experimental Comparison ◽  
Sensing Technology ◽  
Ecological Protection ◽  
Local Optimal Solution ◽  
Remote Sensing Image Processing

Application of Remote Sensing Technology in the Development of Urban Functions is presented. Remote sensing image processing for ecological protection and monitoring, this paper proposes a remote sensing image landmark segmentation algorithm based on the IGSA and PCNN. Because the GSA algorithm has the disadvantages of premature convergence and is easy to fall into the local optimal solution, the improved IGSA algorithm is used to extract the ratio of the image entropy and energy to IGSA, and the entropy change value is used as the IGSA algorithm. Based on the global search ability of IGSA, the optimal value of the key parameters affecting the segmentation effect in PCNN model is found. Finally, through experimental comparison, the proposed method has strong advantages in segmentation effect, real-time and robustness

scholarly journals A simple, effective distance and density based outlier detection algorithm

2021 ◽  
Vol 24 (2) ◽  
pp. 1141
Author(s):  
Sajidha S. A. ◽  
Udai Agarwal ◽  
Pruthviraj R. P. ◽  
Sparsh Agarwal ◽  
Nisha V. M. ◽  
...  
Keyword(s):  
Data Model ◽  
Clustering Algorithms ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Data Set ◽  
Data Points ◽  
Novel Method ◽  
Auc Value ◽  
Anomalous Nature

Outliers are eccentric data points with anomalous nature. Clustering with outliers has received a lot of attention in the data processing community. But, they inordinately affect the quality of the results obtained in case of popular clustering algorithms during the process of finding an optimal solution. In this work, we propose a novel method to classify the data points with grouping characteristics as either an outlier or not. We use both distance and density of a particular data point with respect to the rest of the data points for this process. Distances are used to find the points at the extremities while the densities are used to identify the data points at the sparsest spaces. Further, every data model has to take into account the aspect of generalization in order to work robustly even in out of the box situations. Hence, our approach provides a generalization aspect to the model. The accuracy of the proposed work is measured using area under curve (AUC) was found the highest for cardioto data set -AUC value-0.90 and second highest AUC value was obtained for Spambase data set -0.52 and several other datasets are used to demonstrate the usage of the model proposed.

Multi-Band Contourlet Transform For Adaptive Remote Sensing Image Denoising

2019 ◽  
Vol 63 (7) ◽  
pp. 1084-1098
Author(s):  
Haijiang Wang ◽  
Jingpu Wang ◽  
Fuqi Yao ◽  
Yongqiang Ma ◽  
Lihong Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Denoising ◽  
Remote Sensing Image ◽  
Contourlet Transform ◽  
Remote Sensing Images ◽  
Denoising Method ◽  
Advantages And Disadvantages ◽  
Multi Resolution Analysis ◽  
Resolution Analysis ◽  
Multi Band

Abstract The ability to remove noise from remote sensing images, while retaining the important features of the images, is becoming increasingly important. In this paper, we introduce the multi-band contourlet transform, a new method for adaptively denoising remote sensing images. We describe existing methods that use multi-resolution analysis transforms for denoising images and discuss their respective advantages and disadvantages. We then introduce our novel denoising method, which exploits the advantages of existing methods. We summarize the results of a comprehensive set of experiments designed to evaluate the performance of our method and compare it with the performance of existing methods. The results demonstrate that our method is superior to existing methods, both in terms of its ability to denoise images and to retain salient features of those images following denoising.

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. 

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