scholarly journals Change Detection Accuracy and Image Properties: A Study Using Simulated Data

2010 ◽  
Vol 2 (6) ◽  
pp. 1508-1529 ◽  
Author(s):  
Abdullah Almutairi ◽  
Timothy A. Warner
Keyword(s):  
Change Detection ◽  
Simulated Data ◽  
Detection Accuracy

Vivid Imagers Are Better at Detecting Salient Changes

2006 ◽  
Vol 27 (4) ◽  
pp. 218-228 ◽  
Author(s):  
Paul Rodway ◽  
Karen Gillies ◽  
Astrid Schepman
Keyword(s):  
Individual Differences ◽  
Change Detection ◽  
Visual Imagery ◽  
Detection Task ◽  
Level Of Detail ◽  
Detection Accuracy ◽  
Change Detection Task ◽  
Term Change ◽  
High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17  s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

scholarly journals Complete Ring Artifacts Reduction Procedure for Lab-Based X-ray Nano CT Systems

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 238
Author(s):  
Jakub Šalplachta ◽  
Tomáš Zikmund ◽  
Marek Zemek ◽  
Adam Břínek ◽  
Yoshihiro Takeda ◽  
...  
Keyword(s):  
High Efficiency ◽  
Image Inpainting ◽  
Simulated Data ◽  
Complementary Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Detection Accuracy ◽  
Reduction Procedure ◽  
X Ray ◽  
Artifacts Reduction ◽  
Ct Data

In this article, we introduce a new ring artifacts reduction procedure that combines several ideas from existing methods into one complex and robust approach with a goal to overcome their individual weaknesses and limitations. The procedure differentiates two types of ring artifacts according to their cause and character in computed tomography (CT) data. Each type is then addressed separately in the sinogram domain. The novel iterative schemes based on relative total variations (RTV) were integrated to detect the artifacts. The correction process uses the image inpainting, and the intensity deviations smoothing method. The procedure was implemented in scope of lab-based X-ray nano CT with detection systems based on charge-coupled device (CCD) and scientific complementary metal–oxide–semiconductor (sCMOS) technologies. The procedure was then further tested and optimized on the simulated data and the real CT data of selected samples with different compositions. The performance of the procedure was quantitatively evaluated in terms of the artifacts’ detection accuracy, the comparison with existing methods, and the ability to preserve spatial resolution. The results show a high efficiency of ring removal and the preservation of the original sample’s structure.

scholarly journals Does perceptual grouping improve visuospatial working memory? Optimized processing or encoding bias

2021 ◽  
Author(s):  
Antonio Prieto ◽  
Vanesa Peinado ◽  
Julia Mayas
Keyword(s):  
Working Memory ◽  
Change Detection ◽  
Visual Working Memory ◽  
Visual Information ◽  
Detection Accuracy ◽  
Visuospatial Working Memory ◽  
Gestalt Grouping ◽  
Color Similarity ◽  
Automatic Encoding ◽  
Change Detection Performance

AbstractVisual working memory has been defined as a system of limited capacity that enables the maintenance and manipulation of visual information. However, some perceptual features like Gestalt grouping could improve visual working memory effectiveness. In two different experiments, we aimed to explore how the presence of elements grouped by color similarity affects the change detection performance of both, grouped and non-grouped items. We combined a change detection task with a retrocue paradigm in which a six item array had to be remembered. An always valid, variable-delay retrocue appeared in some trials during the retention interval, either after 100 ms (iconic-trace period) or 1400 ms (working memory period), signaling the location of the probe. The results indicated that similarity grouping biased the information entered into the visual working memory, improving change detection accuracy only for previously grouped probes, but hindering change detection for non-grouped probes in certain conditions (Exp. 1). However, this bottom-up automatic encoding bias was overridden when participants were explicitly instructed to ignore grouped items as they were irrelevant for the task (Exp. 2).

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 BUILDING CHANGE DETECTION FROM BITEMPORAL AERIAL IMAGES USING DEEP LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 565-571
Author(s):  
S. Su ◽  
T. Nawata ◽  
T. Fuse
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Urban Areas ◽  
Network Architecture ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Detection Model ◽  
Wide Range ◽  
Surface Models ◽  
Data Source

Abstract. Automatic building change detection has become a topical issue owing to its wide range of applications, such as updating building maps. However, accurate building change detection remains challenging, particularly in urban areas. Thus far, there has been limited research on the use of the outdated building map (the building map before the update, referred to herein as the old-map) to increase the accuracy of building change detection. This paper presents a novel deep-learning-based method for building change detection using bitemporal aerial images containing RGB bands, bitemporal digital surface models (DSMs), and an old-map. The aerial images have two types of spatial resolutions, 12.5 cm or 16 cm, and the cell size of the DSMs is 50 cm × 50 cm. The bitemporal aerial images, the height variations calculated using the differences between the bitemporal DSMs, and the old-map were fed into a network architecture to build an automatic building change detection model. The performance of the model was quantitatively and qualitatively evaluated for an urban area that covered approximately 10 km2 and contained over 21,000 buildings. The results indicate that it can detect the building changes with optimum accuracy as compared to other methods that use inputs such as i) bitemporal aerial images only, ii) bitemporal aerial images and bitemporal DSMs, and iii) bitemporal aerial images and an old-map. The proposed method achieved recall rates of 89.3%, 88.8%, and 99.5% for new, demolished, and other buildings, respectively. The results also demonstrate that the old-map is an effective data source for increasing building change detection accuracy.

scholarly journals Object-oriented change detection based on weighted polarimetric scattering differences on POLSAR images

2015 ◽  
Vol XL-7/W4 ◽  
pp. 149-154 ◽  
Author(s):  
X. Shi ◽  
L. Lu ◽  
S. Yang ◽  
G. Huang ◽  
Z. Zhao
Keyword(s):  
Change Detection ◽  
Covariance Matrix ◽  
Polarization State ◽  
Object Oriented ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Processing Technologies ◽  
The Difference ◽  
Sar Imagery ◽  
Reasonable Choice

For wide application of change detection with SAR imagery, current processing technologies and methods are mostly based on pixels. It is difficult for pixel-based technologies to utilize spatial characteristics of images and topological relations of objects. Object-oriented technology takes objects as processing unit, which takes advantage of the shape and texture information of image. It can greatly improve the efficiency and reliability of change detection. Recently, with the development of polarimetric synthetic aperture radar (PolSAR), more backscattering features on different polarization state can be available for usage of object-oriented change detection study. In this paper, the object-oriented strategy will be employed. Considering the fact that the different target or target's state behaves different backscattering characteristics dependent on polarization state, an object-oriented change detection method that based on weighted polarimetric scattering difference of PolSAR images is proposed. The method operates on the objects generated by generalized statistical region merging (GSRM) segmentation processing. The merit of GSRM method is that image segmentation is executed on polarimetric coherence matrix, which takes full advantages of polarimetric backscattering features. And then, the measurement of polarimetric scattering difference is constructed by combining the correlation of covariance matrix and the difference of scattering power. Through analysing the effects of the covariance matrix correlation and the scattering echo power difference on the polarimetric scattering difference, the weighted method is used to balance the influences caused by the two parts, so that more reasonable weights can be chosen to decrease the false alarm rate. The effectiveness of the algorithm that proposed in this letter is tested by detection of the growth of crops with two different temporal radarsat-2 fully PolSAR data. First, objects are produced by GSRM algorithm based on the coherent matrix in the pre-processing. Then, the corresponding patches are extracted in two temporal images to measure the differences of objects. To detect changes of patches, a difference map is created by means of weighted polarization scattering difference. Finally, the result of change detection can be obtained by threshold determining. The experiments show that this approach is feasible and effective, and a reasonable choice of weights can improve the detection accuracy significantly.

scholarly journals VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION

2016 ◽  
Vol XLI-B1 ◽  
pp. 703-710 ◽  
Author(s):  
A. Schlichting ◽  
C. Brenner
Keyword(s):  
Change Detection ◽  
Reference Data ◽  
Correlation Method ◽  
Simulated Data ◽  
Detection Algorithm ◽  
Training Data ◽  
Image Correlation ◽  
Data Generation ◽  
Vehicle Localization ◽  
Dynamic Objects

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. <br><br> For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

scholarly journals Modified S2CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection

2018 ◽  
Vol 10 (9) ◽  
pp. 3301 ◽  
Author(s):  
Honglyun Park ◽  
Jaewan Choi ◽  
Wanyong Park ◽  
Hyunchun Park
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Seasonal Effects ◽  
Detection Accuracy ◽  
Difference Image ◽  
Before And After ◽  
Change Detection Algorithms ◽  
The Difference ◽  
Direction Information

This study aims to reduce the false alarm rate due to relief displacement and seasonal effects of high-spatial-resolution multitemporal satellite images in change detection algorithms. Cross-sharpened images were used to increase the accuracy of unsupervised change detection results. A cross-sharpened image is defined as a combination of synthetically pan-sharpened images obtained from the pan-sharpening of multitemporal images (two panchromatic and two multispectral images) acquired before and after the change. A total of four cross-sharpened images were generated and used in combination for change detection. Sequential spectral change vector analysis (S2CVA), which comprises the magnitude and direction information of the difference image of the multitemporal images, was applied to minimize the false alarm rate using cross-sharpened images. Specifically, the direction information of S2CVA was used to minimize the false alarm rate when applying S2CVA algorithms to cross-sharpened images. We improved the change detection accuracy by integrating the magnitude and direction information obtained using S2CVA for the cross-sharpened images. In the experiment using KOMPSAT-2 satellite imagery, the false alarm rate of the change detection results decreased with the use of cross-sharpened images compared to that with the use of only the magnitude information from the original S2CVA.

scholarly journals Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images

2019 ◽  
Vol 11 (2) ◽  
pp. 142 ◽  
Author(s):  
Wenping Ma ◽  
Hui Yang ◽  
Yue Wu ◽  
Yunta Xiong ◽  
Tao Hu ◽  
...  
Keyword(s):  
Change Detection ◽  
Representation Learning ◽  
Local Information ◽  
Learning Ability ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Image Block ◽  
Sar Images ◽  
Difference Image ◽  
Multi Scale

In this paper, a novel change detection approach based on multi-grained cascade forest(gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detectsthe changed and unchanged areas of the images by using the well-trained gcForest. Most existingchange detection methods need to select the appropriate size of the image block. However, thesingle size image block only provides a part of the local information, and gcForest cannot achieve agood effect on the image representation learning ability. Therefore, the proposed approach choosesdifferent sizes of image blocks as the input of gcForest, which can learn more image characteristicsand reduce the influence of the local information of the image on the classification result as well.In addition, in order to improve the detection accuracy of those pixels whose gray value changesabruptly, the proposed approach combines gradient information of the difference image with theprobability map obtained from the well-trained gcForest. Therefore, the image edge information canbe enhanced and the accuracy of edge detection can be improved by extracting the image gradientinformation. Experiments on four data sets indicate that the proposed approach outperforms otherstate-of-the-art algorithms.

scholarly journals Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 1987 ◽  
Author(s):  
Rocío Ramos-Bernal ◽  
René Vázquez-Jiménez ◽  
Raúl Romero-Calcerrada ◽  
Patricia Arrogante-Funes ◽  
Carlos Novillo
Keyword(s):  
Change Detection ◽  
Vegetation Index ◽  
Thermal Emission ◽  
Principal Component ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Types ◽  
Statistical Parameters ◽  
Wide Range ◽  
The World

Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.

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