Intra-Urban Change Analysis Using Sentinel-1 and Nighttime Light Data

Che, Meiqin; Gamba, Paolo

doi:10.1109/jstars.2019.2899881

Cited by 21 publications

(15 citation statements)

References 15 publications

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“…Additionally, the European Space Agency launched the Sentinel sensors, making it easier to obtain high-resolution satellite images [31]. Thus, many scholars use recent images provided by Sentinel satellites to monitor green space changes and urban expansion [32][33][34][35][36]. The use of SPOT imagery and these recently available data enables us to monitor urban expansion and green space change over long periods.…”

Section: Introductionmentioning

confidence: 99%

A Methodology to Monitor Urban Expansion and Green Space Change Using a Time Series of Multi-Sensor SPOT and Sentinel-2A Images

et al. 2019

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Monitoring urban expansion and greenspace change is an urgent need for planning and decision-making. This paper presents a methodology integrating Principal Component Analysis (PCA) and hybrid classifier to undertake this kind of work using a sequence of multi-sensor SPOT images (SPOT-2,3,5) and Sentinel-2A data from 1996 to 2016 in Hangzhou City, which is the central metropolis of the Yangtze River Delta in China. In this study, orthorectification was first applied on the SPOT and Sentinel-2A images to guarantee precise geometric correction which outperformed the conventional polynomial transformation method. After pre-processing, PCA and hybrid classifier were used together to enhance and extract change information. Accuracy assessment combining stratified random and user-defined plots sampling strategies was performed with 930 reference points. The results indicate reasonable high accuracies for four periods. It was further revealed that the proposed method yielded higher accuracy than that of the traditional post-classification comparison approach. On the whole, the developed methodology provides the effectiveness of monitoring urban expansion and green space change in this study, despite the existence of obvious confusions that resulted from compound factors.

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Section: Introductionmentioning

confidence: 99%

A Methodology to Monitor Urban Expansion and Green Space Change Using a Time Series of Multi-Sensor SPOT and Sentinel-2A Images

et al. 2019

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“…Specifically, it has been proved that it is now possible to decouple changes due to urban expansion from those due to an increasing economic activity (Frolking et al, 2013, Huang et al, 2016. Along this research line, and following our previous work (Che, Gamba, 2019), in this paper multi-temporal Sentinel-1 Synthetic Aperture C-band data and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime measurements (also called the Day/Night Band, or DNB) are combined to explore urban change patterns at the geographical scale of the so-called "megacities" or "megalopolis". The joint use of heterogeneous sensors allows an unsupervised discover of spatio-temporal features and deeper relationships between urban constructions and nighttime-light changes, which in turn reveal the connections between built-up area changes and economic development.…”

Section: Introductionmentioning

confidence: 59%

“…The procedure used in this work relies on the processing chain described in a previous work by the same authors (Che, Gamba, 2019). First of all, Sentinel-1 SAR is used to extract urban extents.…”

Section: Methodsmentioning

confidence: 99%

“…The peculiar step introduced in this work is a more effective unsupervised clustering as opposed to the paper (Che, Gamba, 2019), and includes the joint exploitation of three different algorithms: K-means, GMM (Gaussian mixed model with EM algorithm) (Pernkopf, Bouchaffra, 2005), VBGM (Variational Bayesian Gaussian Mixture) (Blei et al, 2017). These techniques are adaptively applied to the same feature space.…”

Section: Methodsmentioning

confidence: 99%

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Change Pattern Exploration With Hierarchical Bi-Clustering on Sentinel-1 Sar and Nighttime Light Data

Che

Gamba

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In the last few decades, urbanization activities have promoted the emergence of megacities, megalopolis, urban clusters or large urban aggregations, but only a few studies have analyzed them using remote sensing data in both the spatial and the temporal domains. In this paper, combining SAR and multispectral sensors with different resolutions, a novel approach, improved by means of a hierarchical clustering technique, is proposed. Urban changes are mapped in the form of multiple spatio-temporal patterns, visualized by change vectors exploiting the combination of SAR and nighttime light data.

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“…In addition, urban light information not only reflects information about human activities, but also indicates details for the location and density of urban buildings. An interesting finding is, Che [180] recognized the changes in night lights are closely related to the functionality of urban buildings, such as factories, agricultural land, residential area. Therefore, the author proposed to combine Sentinel-1 data with night light data, coming to fruition the multi-classification change detection.…”

Section: Fusion Of Other Information and Rs Imagesmentioning

confidence: 97%

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020

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Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research.

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Intra-Urban Change Analysis Using Sentinel-1 and Nighttime Light Data

Cited by 21 publications

References 15 publications

A Methodology to Monitor Urban Expansion and Green Space Change Using a Time Series of Multi-Sensor SPOT and Sentinel-2A Images

A Methodology to Monitor Urban Expansion and Green Space Change Using a Time Series of Multi-Sensor SPOT and Sentinel-2A Images

Change Pattern Exploration With Hierarchical Bi-Clustering on Sentinel-1 Sar and Nighttime Light Data

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

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