Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

Dahiya, Neha; Singh, Sartajvir; Gupta, Sheifali; Rajab, Adel; Hamdi, Mohammed; Elmagzoub, M. A.; Sulaiman, Adel; Shaikh, Asadullah

doi:10.3390/rs15051326

Cited by 11 publications

(6 citation statements)

References 89 publications

(94 reference statements)

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“…The simplest type of artificial neural network is a feedforward neural network, in which the direction of information movement is only forward (Dahiya et al, 2023). In this article, deep neural networks are used to classify cracks in concrete structures.…”

Section: Methodsmentioning

confidence: 99%

“…Artificial neural networks, inspired by how the biological nervous system works, are a particular type of learning model for information processing (Kanwisher et al ., 2023). The simplest type of artificial neural network is a feedforward neural network, in which the direction of information movement is only forward (Dahiya et al ., 2023). In this article, deep neural networks are used to classify cracks in concrete structures.…”

Section: Methodsmentioning

confidence: 99%

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A shallow 2D-CNN network for crack detection in concrete structures

Honarjoo,

Darvishan

2024

IJSI

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PurposeThis study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.Design/methodology/approachThis paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.FindingsResults show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.Originality/valueThis paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A shallow 2D-CNN network for crack detection in concrete structures

Honarjoo,

Darvishan

2024

IJSI

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“…Building on this foundation, they later introduced an enhanced version employing collaborative multi-temporal segmentation and hierarchical compound classification to bolster classification accuracy [36]. Similarly, Dahiya et al [37] presented a post-classification comparison method predicated on artificial neural networks, demonstrating high detection accuracy on the Hyperion EO-1 dataset.…”

Section: > Tgrs-2023-04757r1mentioning

confidence: 99%

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Zhang,

Liu,

Niu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Vector Polygons are valuable survey data, serving as crucial outputs of national geographical censuses and a fundamental data source for detecting changes in geographical conditions. Current remote-sensing image change detection methods rely on comparing images but overlook abundant historical vector results, struggle with model generalization, and lack adequate samples. Consequently, change detection remains a manual process primarily, unable to meet the requirements for automated and efficient monitoring of standardized geographical conditions. Hence, this paper proposes a change detection method for land cover vector polygons based on high-resolution remote sensing images and deep learning. Initially, the enhanced simple linear iterative clustering (SLIC) algorithm is applied to segment dual-temporal images from identical regions. Subsequently, an annotated dataset is generated using a multi-scale extraction, cropping-with-inpainting approach. Next, datasets derived from pre-and post-temporal images are used for training and testing, respectively, and the training set is purified by using twoclassifier cross-validation. Finally, an improved object-oriented convolutional neural network (CNN) model performs finegrained scene classification. The change rules and postprocessing method are then integrated to identify changed vector polygons. To validate the effectiveness and superiority of the proposed method, we conducted experiments on land cover change detection using datasets from two study areas. The results indicate that the proposed method achieves precision and recall rates of 91.89% and 94.44% on dataset-1, respectively. Similarly, in dataset-2, the precision and recall rates reach 87.59% and 91.41%, respectively. These findings demonstrate the method's efficacy in detecting changed vector polygons, reducing manual intervention, and enhancing detection efficiency.

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“…It is a data-driven approach that learns from a large number of training samples of known categories to build a classifier model, which is then applied to new unknown samples for classification [20,21]. Common classifiers include decision tree (DT) [22], maximum-likelihood estimation (MLE) [23], artificial neural network (ANN) [24], support vector machine (SVM) [25], and random forest (RF) [26]. Machine learning classification has higher adaptivity and scalability.…”

Section: Introductionmentioning

confidence: 99%

Forest Cover Change Monitoring Using Sub-Pixel Mapping with Edge-Matching Correction

Xia,

Yang,

Zhang

et al. 2023

Forests

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Sentinel-2 serves as a crucial data source for monitoring forest cover change. In this study, a sub-pixel mapping of forest cover is performed on Sentinel-2 images, downscaling the spatial resolution of the positioned results to 2.5 m, enabling sub-pixel-level forest cover monitoring. A novel sub-pixel mapping with edge-matching correction is proposed on the basis of the Sentinel-2 images, combining edge-matching technology to extract the forest boundary of Jilin-1 images at sub-meter level as spatial constraint information for sub-pixel mapping. This approach enables accurate mapping of forest cover, surpassing traditional pixel-level monitoring in terms of accuracy and robustness. The corrected mapping method allows more spatial detail to be restored at forest boundaries, monitoring forest changes at a smaller scale, which is highly similar to actual forest boundaries on the surface. The overall accuracy of the modified sub-pixel mapping method reaches 93.15%, an improvement of 1.96% over the conventional Sub-pixel-pixel Spatial Attraction Model (SPSAM). Additionally, the kappa coefficient improved by 0.15 to reach 0.892 during the correction. In summary, this study introduces a new method of forest cover monitoring, enhancing the accuracy and efficiency of acquiring forest resource information. This approach provides a fresh perspective in the field of forest cover monitoring, especially for monitoring small deforestation and forest degradation activities.

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Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

Cited by 11 publications

References 89 publications

A shallow 2D-CNN network for crack detection in concrete structures

A shallow 2D-CNN network for crack detection in concrete structures

Land Cover Change Detection Based on Vector Polygons and Deep Learning With High-Resolution Remote Sensing Images

Forest Cover Change Monitoring Using Sub-Pixel Mapping with Edge-Matching Correction

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