Targeted information collection for nuclear verification: a combination of object-based images analysis and pixel-based change detection with very high resolution satellite data exemplified for Iranian nuclear sites

2006 ◽  
Author(s):  
Sven Nussbaum ◽  
Irmgard Niemeyer ◽  
Morton J. Canty
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Satellite Data ◽  
Information Collection ◽  
Object Based ◽  
Very High

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.

scholarly journals Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

2016 ◽  
Vol 8 (6) ◽  
pp. 513 ◽  
Author(s):  
Manuel Aguilar ◽  
Abderrahim Nemmaoui ◽  
Antonio Novelli ◽  
Fernando Aguilar ◽  
Andrés García Lorca
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Satellite Data ◽  
Landsat 8 ◽  
Object Based ◽  
Very High

scholarly journals Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 189 ◽  
Author(s):  
Chi Zhang ◽  
Shiqing Wei ◽  
Shunping Ji ◽  
Meng Lu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Classification ◽  
Remote Sensing Images ◽  
Urban Land Cover ◽  
Object Based ◽  
Very High ◽  
Change Map

The study investigates land use/cover classification and change detection of urban areas from very high resolution (VHR) remote sensing images using deep learning-based methods. Firstly, we introduce a fully Atrous convolutional neural network (FACNN) to learn the land cover classification. In the FACNN an encoder, consisting of full Atrous convolution layers, is proposed for extracting scale robust features from VHR images. Then, a pixel-based change map is produced based on the classification map of current images and an outdated land cover geographical information system (GIS) map. Both polygon-based and object-based change detection accuracy is investigated, where a polygon is the unit of the GIS map and an object consists of those adjacent changed pixels on the pixel-based change map. The test data covers a rapidly developing city of Wuhan (8000 km2), China, consisting of 0.5 m ground resolution aerial images acquired in 2014, and 1 m ground resolution Beijing-2 satellite images in 2017, and their land cover GIS maps. Testing results showed that our FACNN greatly exceeded several recent convolutional neural networks in land cover classification. Second, the object-based change detection could achieve much better results than a pixel-based method, and provide accurate change maps to facilitate manual urban land cover updating.

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.

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