Pixel-Based Change Detection Methods

İlsever, Murat; Ünsalan, Cem

doi:10.1007/978-1-4471-4255-3_2

Cited by 25 publications

(7 citation statements)

References 7 publications

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“…This method has been used extensively for change detection in various applications, including background subtraction for movement detection and remote sensing. As outlined in [29], two images of the same scene taken at separate times t 1 and t 2 are subtracted pixelwise. After the subtraction, the magnitude of the difference value is compared against a threshold.…”

Section: Image Differencementioning

confidence: 99%

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Attard

Debono

Valentino

et al. 2021

Sensors

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Tunnel structural health inspections are predominantly done through periodic visual observations, requiring humans to be physically present on-site, possibly exposing them to hazardous environments. These surveys are subjective (relying on the surveyor experience), time-consuming, and may demand operation shutdown. These issues can be mitigated through accurate automatic monitoring and inspection systems. In this work, we propose a remotely operated machine vision change detection application to improve the structural health monitoring of tunnels. The vision-based sensing system acquires the data from a rig of cameras hosted on a robotic platform that is driven parallel to the tunnel walls. These data are then pre-processed using image processing and deep learning techniques to reduce nuisance changes caused by light variations. Image fusion techniques are then applied to identify the changes occurring in the tunnel structure. Different pixel-based change detection approaches are used to generate temporal change maps. Decision-level fusion methods are then used to combine these change maps to obtain a more reliable detection of the changes that occur between surveys. A quantitative analysis of the results achieved shows that the proposed change detection system achieved a recall value of 81%, a precision value of 93% and an F1-score of 86.7%.

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Section: Image Differencementioning

confidence: 99%

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Attard

Debono

Valentino

et al. 2021

Sensors

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“…We combine the binary masks obtained from previous steps to I t1 and I t2 then taking their difference as follows [13]:…”

Section: Image Differencementioning

confidence: 99%

Combining Common Sense Rules and Machine Learning to Understand Object Manipulation

Sárkány¹,

Csákvári²,

Olasz

2019

Acta Cybern

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Automatic situation understanding in videos has improved remarkably in recent years. However, state-of-the-art methods still have considerable shortcomings: they usually require training data for each object class present and may have high false positive or false negative rates, making them impractical for general applications. We study a case that has a limited goal in a narrow context and argue about the complexity of the general problem. We suggest to solve this problem by including common sense rules and by exploiting various state-of-the art deep neural networks (DNNs) as the detectors of the conditions of those rules. We want to deal with the manipulation of unknown objects at a remote table. We have two action types to be detected: `picking up an object from the table' and `putting an object onto the table' and due to remote monitoring, we consider monocular observation. We quantitatively evaluate the performance of the system on manually annotated video segments, present precision and recall scores. We also discuss issues on machine reasoning. We conclude that the proposed neural-symbolic approach a) diminishes the required size of training data and b) enables new applications where labeled data are difficult or expensive to get.

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“…Moreover, methods suitable for land disturbance monitoring with multi-temporal remote sensing have been proposed, focusing on the accurate detection of the position or time point of disturbances [19][20][21][22][23][24][25]. Earlier algebra-based and statistics-based methods, including image differencing, image rationing, and Change Vector Analysis (CVA), applied the threshold selection procedure to detect disturbed areas [26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Spatio-Temporal Dynamics of Shale Oil/Gas Development with Landsat Time Series: Case Studies in the USA

Wang¹,

Liu²,

Zhang³

et al. 2022

Remote Sensing

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Shale oil/gas extraction has expanded rapidly in the last two decades due to the rising energy prices and the advancement of technologies. Its development can have huge impacts on and, at the same time, is also deeply affected by energy markets, especially in an era with high economic uncertainty. Understanding and monitoring shale oil/gas development over large regions are critical for both energy policies and environmental protection. However, there are currently no applicable methods to track the spatio-temporal dynamics of shale oil/gas development. To fill this gap, we propose a new NDVI Trajectroy Matching algorithm to track shale oil/gas development using the annual Landsat NDVI composite time series from 2000 to 2020. The results reveal that our algorithm can accurately extract the location and time of shale oil/gas exploitation in Eagle Ford and Three Forks, with an accuracy of 83.80% and 81.40%, respectively. In the Eagle Ford area, accuracy for all disturbance year detection was greater than 66.67%, with the best in 2011 and 2019 at 90.00%. The lowest accuracy in the Three Forks area was 63.33% in 2002, while the highest accuracy was 93.33% in 2019. In conclusion, the algorithm can effectively track shale oil/gas development with considerable accuracy and simplicity. We believe that the algorithm has enormous potential for other applications, such as built-up regions, forests, farmlands, and water body expansion and contraction involving vegetation damage.

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Pixel-Based Change Detection Methods

Cited by 25 publications

References 7 publications

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Combining Common Sense Rules and Machine Learning to Understand Object Manipulation

Monitoring the Spatio-Temporal Dynamics of Shale Oil/Gas Development with Landsat Time Series: Case Studies in the USA

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