A novel unsupervised binary change detection method for VHR optical remote sensing imagery over urban areas

Fang, Hong; Du, Peijun; Wang, Xin

doi:10.1016/j.jag.2022.102749

Cited by 20 publications

(9 citation statements)

References 47 publications

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“…a) FCM clustering: The FCM clustering algorithm [37], [54], [55] is based on the memberships of pixels in the difference map. Specifically, FCM clustering is performed to cluster D into three categories: ω c , ω u , and ω n .…”

Section: B Change Extractionmentioning

confidence: 99%

“…The RF classifier [46] is used as Γ(•) because it usually produces accuracy and robust classification performance with superior stability. Of course, other basic classifiers (such as a light-weight CNN [55] or a Siamese CNN [37]) can also be used for change map extraction. Thus, the RF classifier is trained using the training set R r .…”

Section: B Change Extractionmentioning

confidence: 99%

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UCDFormer: Unsupervised Change Detection Using a Transformer-Driven Image Translation

Xu,

Shi,

Guo

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Change detection (CD) by comparing two bitemporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervised CD has attracted extensive attention in the community. However, existing unsupervised CD approaches rarely consider the seasonal and style differences incurred by the illumination and atmospheric conditions in multi-temporal images. To this end, we propose a change detection with domain shift setting for remote sensing images. Furthermore, we present a novel unsupervised CD method using a light-weight transformer, called UCDFormer. Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency. After image translation, we can generate the difference map between the translated before-event image and the original after-event image. Then, a novel reliable pixel extraction module is proposed to select significantly changed/unchanged pixel positions by fusing the pseudo change maps of fuzzy c-means clustering and adaptive threshold. Finally, a binary change map is obtained based on these selected pixel pairs and a binary classifier. Experimental results on different unsupervised CD tasks with seasonal and style changes demonstrate the effectiveness of the proposed UCDFormer. For example, compared with several other related methods, UCDFormer improves performance on the Kappa coefficient by more than 12%. In addition, UCDFormer achieves excellent performance for earthquake-induced landslide detection when considering large-scale applications. The code is available at https://github.com/zhu-xlab/UCDFormer.

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Section: B Change Extractionmentioning

confidence: 99%

Section: B Change Extractionmentioning

confidence: 99%

UCDFormer: Unsupervised Change Detection Using a Transformer-Driven Image Translation

Xu,

Shi,

Guo

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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“…Unsupervised learning is based on clustering analysis of the spectral features of hyperspectral images, which can solve the problem of the lack of deep-sea-hyperspectral-label data [34,35]. Ye et al [36] used an unsupervised algorithm to classify and identify manganese nodules on the ocean floor, but the unsupervised classification lacks a priori true value, and the classification accuracy is often lower than that of supervised classification [37].…”

Section: Introductionmentioning

confidence: 99%

Accurate Identification Method of Small-Size Polymetallic Nodules Based on Seafloor Hyperspectral Data

Sun,

Wu,

Wang

et al. 2024

JMSE

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Polymetallic nodules are spherical or ellipsoidal mineral aggregates formed naturally in deep-sea environments. They contain a variety of metallic elements and are important solid mineral resources on the seabed. How best to quickly and accurately identify polymetallic nodules is one of the key questions of marine development and deep-sea-mineral-resource utilization. We propose a method that uses YOLOv5s as a reference network and integrates the IoU (Intersection over Union) and the Wasserstein distance in the optimal transmission theory to accurately identify different sizes of polymetallic nodules. Experiment using deep-sea hyperspectral data obtained from the Peru Basin was performed. The results showed that better recognition effects were achieved when the fusion ratio of overlap and Wasserstein distance metric was 0.5, and the accuracy of the proposed algorithm reached 84.5%, which was 6.2% higher than that of the original baseline network. In addition, the rest of the performance indexes were also improved significantly compared to traditional methods.

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“…Ref. [38] conducted change detection in urban areas based on VHR. Kurgans (burial mounds of ancient civilizations) in the Altai region were detected based on the deep convolutional neural network (CNN) technique, and it was demonstrated that CNN-based object detection can largely narrow down the search area for archaeologists in unsurveyed regions; therefore, it is useful for preparing field-survey campaigns and guiding archaeological fieldwork [39].…”

Section: Introductionmentioning

confidence: 99%

Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model

Guo

Luo

et al. 2022

Remote Sensing

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As a large-scale irrigation and water conservancy project in ancient times, karez are common in Central Asia and arid regions with a history of thousands of years. Turpan, which is located in the Xinjiang Uyghur Autonomous Region, has the most extensive and concentrated distribution of karez shafts in China. There are tens of thousands of shafts, some of which are in use and are living cultural heritage. According to radiocarbon (14C) dating, some karezs are over 600 years old. The karez is of great significance to the research on geology, hydrology, oasis, climate change, and development history of karez in Turpan. With the development of the population, arable land, industrialization, and urbanization, karez systems are facing the risk of abandonment. Detailed karez distribution mapping or dynamic monitoring data are important for their management or analysis; although there are related methods, due to Turpan’s large desert and “Gobi” environments, field surveys are time- and energy-consuming, and some areas are difficult to access. Precise shaft locations and distribution maps are scarce and often lack georeferencing. The distribution and preservation of karez have not yet been fully grasped. In this study, we evaluated the effectiveness of You Only Look Once version 5 (YOLOv5) in automatically detecting karez in high-resolution images of the Turpan region. We propose post-processing steps to reduce the false karez identified by YOLOv5. Our results demonstrate the feasibility of using YOLOv5 and post-processing techniques to detect karez automatically, and the detected results are sufficient to capture the linear alignment of karez. Target detection based on YOLOv5 and post-processing can greatly improve automatic shaft identification and is therefore useful for the fine mapping of karez. We also applied this method in Shanshan County (for which no detailed mapping data on karez has been obtained before) and successfully detected some karez that had not been archived before. The number of shafts in Turpan is 82,493. Through DBSCAN clustering, it was identified which karez line belonged to which shaft; the number of sections of karez that have been used is 5057, which have a total length of 2387.2 km. The karez line obtained was overlaid with the crop-land data, and the positional relationship between the karez line and the crop land was analyzed. The cultivated area is basically surrounded by karez. Our method can potentially be applied to construct an inventory for all karez shafts globally.

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A novel unsupervised binary change detection method for VHR optical remote sensing imagery over urban areas

Cited by 20 publications

References 47 publications

UCDFormer: Unsupervised Change Detection Using a Transformer-Driven Image Translation

UCDFormer: Unsupervised Change Detection Using a Transformer-Driven Image Translation

Accurate Identification Method of Small-Size Polymetallic Nodules Based on Seafloor Hyperspectral Data

Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model

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