Multispectral Image Change Detection Based on Single-Band Slow Feature Analysis

He, Youxi; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

doi:10.3390/rs13152969

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…Many experiments proved that DSFA has a good effect on detection. In 2021, He et al proposed a single-band slow feature analysis method [18]. Each band was extracted for slow feature analysis, and the change region was obtained by a filter and clustering method.…”

Section: Related Workmentioning

confidence: 99%

Remote Sensing Change Detection Based on Unsupervised Multi-Attention Slow Feature Analysis

et al. 2022

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With the development of big data, analyzing the environmental benefits of transportation systems by artificial intelligence has become a hot issue in recent years. The ground traffic changes can be overlooked from a high-altitude perspective, using the technology of multi-temporal remote sensing change detection. We proposed a novel unsupervised algorithm by combining the image transformation and deep learning method. The new algorithm for remote sensing images is named multi-attention slow feature analysis (ASFA). In this model, three parts perform different functions respectively. The first part records to the K-BoVW to classify the categories of the ground objects as a channel parameter. The second part is a residual convolution with multiple attention mechanisms including temporal, spatial, and channel attention. Feature extraction and updating are completed at this link. Finally, we put the updated features in the slow feature analysis to highlight the variant components which we want and then generate the change map visually. Experiments on three very high-resolution datasets verified that the ASFA has a better performance than four basic change detection algorithms and an improved SFA algorithm. More importantly, this model works well for traffic road detection and helps us analyze the environmental benefits of traffic changes.

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Section: Related Workmentioning

confidence: 99%

Remote Sensing Change Detection Based on Unsupervised Multi-Attention Slow Feature Analysis

et al. 2022

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“…To help judge the results of the change detection, five objective indicators were used as measures, namely, false positives (FPs) [50,62], false negatives (FNs) [50,63], overall errors (OEs) [50,64], percentage correct classification (PCC) [50,65], and kappa coefficient (KC) [50,66]. In the binary ground-truth image, we calculated the actual number of pixels belonging to the unchanged class (Nu) and the changed class (Nc).…”

Section: Experimental Settingsmentioning

confidence: 99%

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

Jia

2021

Remote Sensing

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Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.

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“…Consequently, environmental factors can impede accurate detection when relying solely on RGB images. In contrast, multispectral images offer a wealth of spectral information [ 6 ], enhanced differentiation capabilities [ 7 ], and improved quantitative analysis abilities [ 8 ]. They exhibit higher levels of detection accuracy and reliability.…”

Section: Introductionmentioning

confidence: 99%

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

Zhao,

Luo,

Wang

et al. 2024

Sensors

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Aircraft failures can result in the leakage of fuel, hydraulic oil, or other lubricants onto the runway during landing or taxiing. Damage to fuel tanks or oil lines during hard landings or accidents can also contribute to these spills. Further, improper maintenance or operational errors may leave oil traces on the runway before take-off or after landing. Identifying oil spills in airport runway videos is crucial to flight safety and accident investigation. Advanced image processing techniques can overcome the limitations of conventional RGB-based detection, which struggles to differentiate between oil spills and sewage due to similar coloration; given that oil and sewage have distinct spectral absorption patterns, precise detection can be performed based on multispectral images. In this study, we developed a method for spectrally enhancing RGB images of oil spills on airport runways to generate HSI images, facilitating oil spill detection in conventional RGB imagery. To this end, we employed the MST++ spectral reconstruction network model to effectively reconstruct RGB images into multispectral images, yielding improved accuracy in oil detection compared with other models. Additionally, we utilized the Fast R-CNN oil spill detection model, resulting in a 5% increase in Intersection over Union (IOU) for HSI images. Moreover, compared with RGB images, this approach significantly enhanced detection accuracy and completeness by 25.3% and 26.5%, respectively. These findings clearly demonstrate the superior precision and accuracy of HSI images based on spectral reconstruction in oil spill detection compared with traditional RGB images. With the spectral reconstruction technique, we can effectively make use of the spectral information inherent in oil spills, thereby enhancing detection accuracy. Future research could delve deeper into optimization techniques and conduct extensive validation in real airport environments. In conclusion, this spectral reconstruction-based technique for detecting oil spills on airport runways offers a novel and efficient approach that upholds both efficacy and accuracy. Its wide-scale implementation in airport operations holds great potential for improving aviation safety and environmental protection.

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Multispectral Image Change Detection Based on Single-Band Slow Feature Analysis

Cited by 7 publications

References 36 publications

Remote Sensing Change Detection Based on Unsupervised Multi-Attention Slow Feature Analysis

Remote Sensing Change Detection Based on Unsupervised Multi-Attention Slow Feature Analysis

Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement

Charge-Coupled Frequency Response Multispectral Inversion Network-Based Detection Method of Oil Contamination on Airport Runway

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