scholarly journals Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index

2020 ◽  
Vol 12 (18) ◽  
pp. 2952
Author(s):  
Aisha Javed ◽  
Sejung Jung ◽  
Won Hee Lee ◽  
Youkyung Han
Keyword(s):  
Change Detection ◽  
Accuracy Assessment ◽  
Principal Component ◽  
False Alarms ◽  
Complex Objects ◽  
Number Of False Alarms ◽  
Object Based ◽  
Change Vector ◽  
New Buildings ◽  
Very High

Change detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. However, with increasing interest in very-high-resolution (VHR) imagery and determining changes in small and complex objects such as buildings or roads, traditional methods showed limitations, for example, the large number of false alarms or noise in the results. Thus, researchers have focused on extending PBCD to OBCD. In this study, we proposed a method for detecting the newly built-up areas by extending PBCD results into an OBCD result through the Dempster–Shafer (D–S) theory. To this end, the morphological building index (MBI) was used to extract built-up areas in multitemporal VHR imagery. Then, three PBCD algorithms, change vector analysis, principal component analysis, and iteratively reweighted multivariate alteration detection, were applied to the MBI images. For the final CD result, the three binary change images were fused with the segmented image using the D–S theory. The results obtained from the proposed method were compared with those of PBCD, OBCD, and OBCD results generated by fusing the three binary change images using the major voting technique. Based on the accuracy assessment, the proposed method produced the highest F1-score and kappa values compared with other CD results. The proposed method can be used for detecting new buildings in built-up areas as well as changes related to demolished buildings with a low rate of false alarms and missed detections compared with other existing CD methods.

scholarly journals Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory

2020 ◽  
Vol 12 (6) ◽  
pp. 983 ◽  
Author(s):  
Youkyung Han ◽  
Aisha Javed ◽  
Sejung Jung ◽  
Sicong Liu
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Principal Component ◽  
Geographic Area ◽  
Majority Voting ◽  
Dempster Shafer Theory ◽  
Object Based ◽  
Intensity Images ◽  
Shafer Theory ◽  
Very High

Change detection (CD), one of the primary applications of multi-temporal satellite images, is the process of identifying changes in the Earth’s surface occurring over a period of time using images of the same geographic area on different dates. CD is divided into pixel-based change detection (PBCD) and object-based change detection (OBCD). Although PBCD is more popular due to its simple algorithms and relatively easy quantitative analysis, applying this method in very high resolution (VHR) images often results in misdetection or noise. Because of this, researchers have focused on extending the PBCD results to the OBCD map in VHR images. In this paper, we present a proposed weighted Dempster-Shafer theory (wDST) fusion method to generate the OBCD by combining multiple PBCD results. The proposed wDST approach automatically calculates and assigns a certainty weight for each object of the PBCD result while considering the stability of the object. Moreover, the proposed wDST method can minimize the tendency of the number of changed objects to decrease or increase based on the ratio of changed pixels to the total pixels in the image when the PBCD result is extended to the OBCD result. First, we performed co-registration between the VHR multitemporal images to minimize the geometric dissimilarity. Then, we conducted the image segmentation of the co-registered pair of multitemporal VHR imagery. Three change intensity images were generated using change vector analysis (CVA), iteratively reweighted-multivariate alteration detection (IRMAD), and principal component analysis (PCA). These three intensity images were exploited to generate different binary PBCD maps, after which the maps were fused with the segmented image using the wDST to generate the OBCD map. Finally, the accuracy of the proposed CD technique was assessed by using a manually digitized map. Two VHR multitemporal datasets were used to test the proposed approach. Experimental results confirmed the superiority of the proposed method by comparing the existing PBCD methods and the OBCD method using the majority voting technique.

Change detection in very high resolution imagery and vector data applied to the monitoring of geographical conditions

Sensor Review ◽  
2016 ◽  
Vol 36 (4) ◽  
pp. 347-358 ◽  
Author(s):  
Zhenzhen Zhao ◽  
Aiwen Lin ◽  
Qin Yan ◽  
Jiandi Feng
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Accuracy Assessment ◽  
Assessment Method ◽  
Chinese Government ◽  
Vector Data ◽  
Content Type ◽  
Image Objects ◽  
Object Based ◽  
Very High

Purpose Geographical conditions monitoring (GCM) has elicited significant concerns from the Chinese Government and is closely related to the “Digital China” program. This research aims to focus on object-based change detection (OBCD) methods integrating very-high-resolution (VHR) imagery and vector data for GCM. Design/methodology/approach The main content of this paper is as follows: a multi-resolution segmentation (MRS) algorithm is proposed for obtaining homogeneous and contiguous image objects in two phases; a post-classification comparison (PCC) method based on the nearest neighbor algorithm and an image-object analysis (IOA) technique based on a differential entropy algorithm are used to improve the accuracy of the change detection; and a vector object-based accuracy assessment method is proposed. Findings Results show that image objects obtained using the MRS algorithm attain the objectives of the “same spectrum within classes” and “different spectrum among classes”. Moreover, the two OBCD methods can detect over 85 per cent of the changed regions. The PCC strategy can obtain the categories of image objects with a high degree of precision. The IOA technique is easy to use and largely automated. Originality/value On the basis of the VHR satellite imagery and vector data, the above methods can effectively and accurately provide technical support for GCM implementation.

scholarly journals Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

2018 ◽  
Vol 7 (11) ◽  
pp. 441 ◽  
Author(s):  
Zhenjin Zhou ◽  
Lei Ma ◽  
Tengyu Fu ◽  
Ge Zhang ◽  
Mengru Yao ◽  
...  
Keyword(s):  
Coral Reef ◽  
Change Detection ◽  
Accuracy Assessment ◽  
Detection Accuracy ◽  
Reef Environment ◽  
Object Based ◽  
Coral Reef Environment ◽  
Study Sites ◽  
Object Area ◽  
Segmentation Image

Despite increases in the spatial resolution of satellite imagery prompting interest in object-based image analysis, few studies have used object-based methods for monitoring changes in coral reefs. This study proposes a high accuracy object-based change detection (OBCD) method intended for coral reef environment, which uses QuickBird and WorldView-2 images. The proposed methodological framework includes image fusion, multi-temporal image segmentation, image differencing, random forests models, and object-area-based accuracy assessment. For validation, we applied the method to images of four coral reef study sites in the South China Sea. We compared the proposed OBCD method with a conventional pixel-based change detection (PBCD) method by implementing both methods under the same conditions. The average overall accuracy of OBCD exceeded 90%, which was approximately 20% higher than PBCD. The OBCD method was free from salt-and-pepper effects and was less prone to images misregistration in terms of change detection accuracy and mapping results. The object-area-based accuracy assessment reached a higher overall accuracy and per-class accuracy than the object-number-based and pixel-number-based accuracy assessment.

scholarly journals Change Detection from Remotely Sensed Images Based on Stationary Wavelet Transform

2017 ◽  
Vol 7 (6) ◽  
pp. 3395
Author(s):  
Abhishek Sharma ◽  
Tarun Gulati
Keyword(s):  
Wavelet Transform ◽  
Change Detection ◽  
High Frequency ◽  
Low Frequency ◽  
Speckle Noise ◽  
False Alarms ◽  
Stationary Wavelet Transform ◽  
Fusion Rules ◽  
Number Of False Alarms ◽  
Log Ratio

The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K<sub>c</sub>). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.

Gaussian process NARX model for damage detection in composite aircraft structures

2021 ◽  
pp. 1-10
Author(s):  
Samuel da Silva ◽  
Luis G G Villani ◽  
Marc Rebillat ◽  
Nazih Mechbal
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Principal Component ◽  
False Alarms ◽  
Confidence Bounds ◽  
Model Uncertainties ◽  
Bottom Part ◽  
Number Of False Alarms ◽  
Simulation Errors ◽  
Nonlinear Autoregressive

Abstract This paper demonstrates the Gaussian process regression model's applicability combined with a nonlinear autoregressive exogenous (NARX) framework using experimental data measured with PZTs' patches bonded in a composite aeronautical structure for concerning a novel SHM strategy. A stiffened carbon-epoxy plate regarding a healthy condition and simulated damage on the center of the bottom part of the stiffener is utilized. Comparing the performance in terms of simulation errors is made to observe if the identified models can represent and predict the waveform with confidence bounds considering the confounding effect produced by noise or possible temperature variations assuming a dataset preprocessed using principal component analysis. The results of the GP-NARX identified model have attested correct classification with a reduced number of false alarms, even with model uncertainties propagation regarding healthy and damaged conditions.

scholarly journals False Alarms Rate Reduction Using Filtered Monitoring Indices

2017 ◽  
Vol 2 (1) ◽  
pp. 40-50
Author(s):  
M. AMMICHE ◽  
A. KOUADRI
Keyword(s):  
Fuzzy Logic ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Principal Component ◽  
Real Data ◽  
False Alarms ◽  
Detection Accuracy ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Number Of False Alarms

False alarms are the major problem in fault detection when using multivariate statistical process monitoring such as principal component analysis (PCA), they affect the detection accuracy and lead to make wrong decisions about the process operation status. In this work, filtering the monitoring indices is proposed to enhance the detection by reducing the number of false alarms. The filters that were used are: Standard Median Filter (SMF), Improved Median Filter (IMF) and fuzzy logic based filter. Signal to Noise Ratio (SNR), False Alarms Rate (FAR) and the detection time of the fault were used as criteria to compare their performance and their filtering action influence on monitoring. The algorithms were applied to cement rotary kiln data; real data, to remove spikes and outliers on the monitoring indices of PCA, and then, the filtered signals were used to supervise the system. The results, in which the fuzzy logic based filter showed a satisfactory performance, are presented and discussed.

scholarly journals Comparative Analysis of Change Detection Techniques In Landuse / Landcover Mapping of Oyo Town, Oyo State, Nigeria

2020 ◽  
pp. 44-62
Author(s):  
Olaniyi Saheed S. ◽  
Igbokwe J. I ◽  
Ojiako J. C.
Keyword(s):  
Principal Component Analysis ◽  
Comparative Analysis ◽  
Change Detection ◽  
Accuracy Assessment ◽  
Principal Component ◽  
Component Analysis ◽  
United States Geological Survey ◽  
Landsat Images ◽  
Detection Techniques ◽  
Analysis Of Change

Landcover is the natural surface of the earth undisturbed by human activities. It represents vegetation, natural or man-made features and every other visible evidence of land use. Landuse on the other hand refers to the use of land by humans while Change detection is the process of identifying differences in the state of an object or phenomenon by observing it in different times. This study is aimed at comparative analysis of change detection techniques in landuse/ landcover mapping of Oyo town with the objectives of comparing and evaluating the results of different change detection techniques as well as production of Landuse/ Landcover map of the study area for the period of 1990 and 2016. Landsat images of 1990, 2003 and 2016 covering the study area (Path 191, Row 54 & 55) were collected from the archives of United States Geological Survey (USGS) agency and image processing and analysis were done using ERDAS Imagine 2015 and ArcGIS 10.5. The results of the study were achieved through image pre-processing, image enhancement, image band combination, change detection through pre-classification (image differencing, image ratioing, Principal Component Analysis) and Post-Classification Comparison (PCC) methods, and results analysed. The result of accuracy assessment in this research shows that a PCA produces a better result of 91.67% while PCC delivered accuracy that ranges between 83.33% and 87.5%. However, PCC gives a better result on the change detection in the study area as it affords more analysis on the study area based on the thematic classes generated for each landuse and landcover of the study area. This study hereby recommends Post-Classification Comparison (PCC) and Principal Component Analysis (PCA) for change detection in the study area. Further research on change detection in the study area should be carried out using Object-Based Image Analysis (OBIA) using high resolution images because this research is hinge on pixel based classification of medium resolution images.

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