Joint exploitation of spaceborne SAR images and GIS techniques for urban coherent change detection

Manzoni, Marco; Guarnieri, Andrea Monti; Molinari, Monia Elisa

doi:10.1016/j.rse.2020.112152

Cited by 24 publications

(10 citation statements)

References 43 publications

(44 reference statements)

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“…For instance, Ansari et al [33] used multi-resolution texture features from polarimetric SAR images to analyze urban changes. On the other side, a simple and fast method was developed to identify structural changes in urban environments using SAR imagery [34]. Urban mapping and delineation with the help of SAR data were presented in other studies as well [30,35,36].…”

Section: Related Workmentioning

confidence: 99%

Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

et al. 2021

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Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.

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Section: Related Workmentioning

confidence: 99%

Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

et al. 2021

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“…Gadal, Sébastien and Ouerghemmi, Walid et.al [20] considering that hyperspectral remote sensing images can describe surface objects and landscapes more accurately, a classification method based on urban target spectral database is proposed to detect and classify specific urban targets. Manzoni, Marco and Monti-Guarnieri, Andrea et.al [21] combining synthetic aperture radar (SAR) images and geospatial information systems, a simple and fast method to identify structural changes in buildings in urban environments is proposed, which can effectively evaluate small changes after disasters.…”

Section: The Traditional Image Perception Of City and Villagesmentioning

confidence: 99%

Exploring Spatiotemporal Change of City and Village from Remote Sensing Using Multi-Branch Networks

Zhao

Tian

2021

Preprint

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With the rapid development of social economy, the urban and rural environment, form and infrastructure have also under- gone earth-shaking changes. As a gathering place for human activities, urban and rural areas play a vital role in the interaction between humans and society. If traditional machine learning methods are used to perceive changes in the intentional connotation of urban and rural areas, it is easy to ignore the detailed information of the intentional target. At the same time, the perception accuracy needs to be improved. Therefore, the deep neural network in this paper proposes a way to perceive the temporal and spatial changes of urban and rural intentional connotations from the perspective of remote sensing. The framework first uses the multi-branch DenseNet to capture the multi-scale spatiotemporal information of the intended target, and embeds it with high-level semantics and low-level physical appearance information. Secondly, a multi-branch cross-channel attention module is designed to perform reﬁned and integrated processing of multi-level spatiotemporal information to realize the aggregation of multi-branch information. Finally, it was tested and verified on two different time data sets, and good performance was achieved.

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“…By combining submeter spatial resolution with extensive temporal resolution, the organic combination of polarized and multi-angle high-dimensional data produces a vast amount of rich information regarding surface changes and trends, as well as improving the detection of those changes. The detection of building changes can rely on highresolution remotely sensed images to serve a variety of purposes including urban planning [2], land use analysis [3], etc.…”

Section: Introductionmentioning

confidence: 99%

Cross spatio-temporal attention network for change detection

Yi,

Yu,

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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Detecting building changes based on high-resolution remote sensing images constitutes one of the most important tools for urban management, methods based on deep learning are substantially more efficient than traditional methods of detecting changes. Nevertheless, there are several problems with most models, such as incorrect edge detection results and missing detection results. Due to continuous down sampling, there is a loss of detailed information in the image as well as an insufficient ability to characterize features. Our paper proposes a multi-scale feature dependency network to improve the performance of change detection models. By combining the multi-scale embedding module with the spatiotemporal attention module, we can extract the original image features, preserve the rich context with multi-scale convolution, and establish long-short distance feature dependencies across different scale patches using long-short distance attention. Furthermore, we introduce a dynamic position deviation calculation method to accurately determine the relative position relationship between features during the attentional action phase. In this manner, the accuracy of model detection will be improved. Multi-level feature fusion helps improve the model's ability to perceive detail by fusing features at different scales, capturing semantic information from these features, and enhancing the feature representation capability. The publicly available building dataset LEVIR-CD was used to conduct comparative experiments with mainstream methods. In both visual detection and quantitative evaluation metrics, our network outperforms the comparison method.

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Joint exploitation of spaceborne SAR images and GIS techniques for urban coherent change detection

Cited by 24 publications

References 43 publications

Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

Exploring Spatiotemporal Change of City and Village from Remote Sensing Using Multi-Branch Networks

Cross spatio-temporal attention network for change detection

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