scholarly journals Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm

2019 ◽  
Vol 7 (1) ◽  
pp. 301-325
Author(s):  
Nailus Sa'ada ◽  
Tri Harsono ◽  
Ahmad Basuki
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Small Groups ◽  
Satellite Image ◽  
Cluster Method ◽  
Dbscan Clustering ◽  
Dbscan Algorithm ◽  
Segmentation Process ◽  
Cloud Temperature ◽  
Segmentation Image

Images contain a lot of information that can be used in a variety of areas. One of the images that have much information inside is satellite image. In order to extract the information properly, the image processing step should be performed properly. The segmentation process plays an important role in image processing, especially for feature extraction. Many ways were developed to perform the segmentation image. In this study, we apply DBSCAN clustering to segment images on whirlwind cloud feature extraction problems. DBSCAN is a density-based classifier method which means it is suitable to group a density-based data. While the image used in the segmentation process is the Himawari 8 satellite image which also contains density-based data. It contains various information about clouds condition like cloud type, cloud temperature, cloud humidity, rainfall potential based on cloud temperature, etc. This study uses Himawari 8 satellite images as input where the images taken are images several hours before a wirlwind event in an area, while the cluster method used is the DBSCAN algorithm. Clustering is done to get the extraction features of a wirlwind in the form of centroid points that characterize the movement of a cloud. Segmentation performance was observed based on the number of centroid points as a result of clustering several types of clouds in an area before a wirlwind occurred. Based on segmentation testing using the DBSCAN algorithm for cloud data in an area for several hours before a wirlwind, better segmentation performance was obtained compared to the segmentation results of the Meng hee heng k-means algorithm for the same test data specifications. DBSCAN separates a type of cloud in more detail that makes it easier to record each centroid of each cluster around the scene. It is even able to cluster small groups of clouds independently so that these small groups of clouds can also be detected as features.

scholarly journals Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques

2021 ◽  
Vol 6 (3) ◽  
pp. 056-062
Author(s):  
Dena Nadir George ◽  
Haitham Salman Chyad ◽  
Raniah Ali Mustafa
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Medical Imaging ◽  
Image Processing Techniques ◽  
Classification Feature ◽  
Segmentation Methods ◽  
Comprehensive Survey ◽  
Segmentation Image ◽  
Processing Techniques ◽  
Cancer Diseases

Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and segmentation methods utilized image processing for cancer diseases for most body's sections are clearly reviewed.

scholarly journals Analisis Perbandingan Kinerja Metode Superpixel dan Gradien berbasis Edge Detector pada Pendeteksian Objek Bergerak

2020 ◽  
Vol 8 (2) ◽  
pp. 362
Author(s):  
MUHAMMAD KHAERUL NAIM MURSALIM ◽  
IHSAN VERDIAN
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Edge Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Edge Detector ◽  
Segmentation Process ◽  
Gradient Based ◽  
Edge Detection Method

ABSTRAKSalah satu bagian dalam algoritma pemrosesan citra adalah proses segmentasi yang menjadi tahap pra-pemrosesan untuk ekstraksi fitur objek. Superpixel menjadi salah satu solusi pada proses segmentasi dengan mendefenisikan kumpulan piksel yang mempunyai kesamaan karekterisitik sehingga membawa banyak informasi mengenai fitur objek. Adapun tantangan yang dihadapi dalam mendeteksi objek bergerak adalah ketidakmampuan untuk memisahkan objek bergerak dari background objek. Sehingga, pada citra yang dideteksi akan dikelilingi oleh wilayah yang terdapat derau. Pada penelitian ini, diusulkan metode superpixel berbasis deteksi tepi untuk mendeteksi objek bergerak. Selanjutnya, kinerja metode superpixel diuji dengan membandingkan dengan metode deteksi tepi yang berbasis gradient. Hasilnya menunjukkan bahwa metode yang diusulkan mampu meminimalisir derau lebih baik dan hasil perhitungan MSE, RMSE, dan PSNR hanya berbeda 0.06% dan 0.1% dari metode Sobel dan Prewitt.Kata kunci: Deteksi tepi, Objek bergerak, Proses Segmentasi, Superpixel ABSTRACTOne part of the image processing is the segmentation which becomes the preprocessing stage for feature extraction. Superpixel becomes solutions in the segmentation process by defining a collection of pixels that have the same characteristics ang bringing the information about the object's features. The challenge faced in detecting moving objects is the inability to separate moving objects from the object's background. Thus, the detected image will be surrounded by an area with noise. In this study, a superpixel-based edge detection method is proposed to detect moving objects. Furthermore, the performance of the superpixel method is tested by comparing it to the gradient-based edge detection method. The results show that the proposed method is able to minimize noise better and the results of MSE, RMSE, and PSNR calculations differ only 0.06% and 0.1% from the Sobel and Prewitt methods.Keywords: Edge detection, Moving objects, Segmentation, Superpixels

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 Research on Rolling Bearing Fault Feature Extraction and Diagnosis Method Based on Image Processing

2021 ◽  
Vol 5 (2) ◽  
pp. 1-7
Author(s):  
Sun Y
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Working Environment ◽  
Ring Rolling ◽  
Rolling Bearings ◽  
Binary Matrix ◽  
The Time Domain

In economic construction, there are many large and important machinery and equipment. Some equipment will continue to work in a harsh working environment, so many and various failures will occur. Rolling bearings are one of the widely used parts in rotating machinery. They are generally composed of inner ring, outer ring, rolling element and holding. The frame is composed of four parts, the failure of the bearing is particularly important, and its safe operation has a vital impact on the entire equipment, Feature extraction is the key link in the subsequent identification of fault types, Although feature extraction in the time domain and frequency domain is effective, it is also necessary to find new feature extraction methods in new areas. On the basis of the snowflake image obtained by using the principle of SDP(Symmetrized Dot Pattern), a method for extracting fault features of rolling bearings based on image processing is proposed, and the snowflake standard map for different working conditions is constructed. The number of snowflake images under different working conditions is different. The binary matrix of the test image is compared with it, and then classified and identified. Finally, the algorithm is validated, and the ideal result is obtained to verify its rationality and effectiveness.

scholarly journals AN OPTIMIZED SURF-BASED FIRE AND SMOKE DETECTION SYSTEM USING IMAGE PROCESSING FROM SURVEILLANCE VIDEO.

2020 ◽  
Vol 9 (10) ◽  
pp. 86-96
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Optimization Approach ◽  
Surveillance Video ◽  
Smoke Detection ◽  
Video Frames ◽  
Optimal Feature

Over the last decades, digital image processing based fire and smoke detection have been improving steadily to provide a more accurate detection results in the area of surveillance security system. Detection of the fire and smoke from the surveillance videos is very challenging task due to the complex structural properties of the video frames or images and need improvisation in the existing work by utilization of feature selection or optimization approach to select on optimal feature according to the fire and smoke. A research based on the combination of various feature extraction techniques with feature selection approach for fire and smoke detection has been presented in this paper. In this research, we develop Fire and Smoke Detection (FSD) system using digital image processing with the concept of Speed up Robust Feature (SURF) along with the Intelligent Water Drops (IWD) as a feature selection and optimization algorithm. Here, Artificial Neural Network (ANN) is used as an Artificial Intelligence (AI) technique with that helps to select a set of optimal feature from the extracted by SURF descriptor from the video frames. By utilizing the concept of optimized ANN, the accuracy of proposed FSD system is increases in terms of detection accuracy and with minimum percentage of error. At last, the performance of the FSD system is calculated to validate the model and this shows that it is possible to use IWD with SURF as a feature extraction technique in order to detect the fire or smoke form the surveillance video with minimum error rate and the simulation results clearly show the effectiveness of proposed FSD system

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