Generation of Pixel-Level SAR Image Time Series Using a Locally Adaptive Matching Technique

Cheng, Lijuan; Li, Manchun; Zhong, Liguo; Wang, Jiechen

doi:10.14358/pers.80.9.839

Cited by 7 publications

(4 citation statements)

References 34 publications

(29 reference statements)

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“…As future work is expected to develop techniques for obtaining more observations, this compensates those affected by partial cloudy conditions. This implies the application of techniques to recover deteriorated images due to atmospheric conditions, and with the resource to radar data from Sentinel-1 (generating time-series of this data, as in [31]). The last may be used just for the image recovery, but also to get more information.…”

Section: Discussionmentioning

confidence: 99%

Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

et al. 2020

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The difficult job of fighting fires and the nearly impossible task to stop a wildfire without great casualties requires an imperative implementation of proactive strategies. These strategies must decrease the number of fires, the burnt area and create better conditions for the firefighting. In this line of action, the Portuguese Institute of Nature and Forest Conservation defined a fire break network (FBN), which helps controlling wildfires. However, these fire breaks are efficient only if they are correctly maintained, which should be ensured by the local authorities and requires verification from the national authorities. This is a fastidious task since they have a large network of thousands of hectares to monitor over a full year. With the increasing quality and frequency of the Earth Observation Satellite imagery with Sentinel-2 and the definition of the FBN, a semi-automatic remote sensing methodology is proposed in this article for the detection of maintenance operations in a fire break. The proposed methodology is based on a time-series analysis, an object-based classification and a change detection process. The change detection is ensured by an artificial neural network, with reflectance bands and spectral indices as features. Additionally, an analysis of several bands and spectral indices is presented to show the behaviour of the data during a full year and in the presence of a maintenance operation. The proposed methodology achieved a relative error lower than 4% and a recall higher than 75% on the detection of maintenance operations.

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Section: Discussionmentioning

confidence: 99%

Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

et al. 2020

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“…To achieve accurate spatial co-registration between images, a relative image to image registration was implemented using the locally adaptive registration method proposed by Cheng et al (2014). These steps include preprocessing, co-registration, and de-noising.…”

Section: Construction Of Pixel-level Sar Image Time Seriesmentioning

confidence: 99%

“…However, optical remote sensing images are influenced by cloud, and it can be difficult to find cloud-free data in the area of interest in certain periods. Most of the studies for change detection utilize the phase information of SAR image data, whereas the backscattering coefficient of pixel-level SAR image time series is rarely used (Julea et al 2011;Cheng et al 2014). Most of the studies for change detection utilize the phase information of SAR image data, whereas the backscattering coefficient of pixel-level SAR image time series is rarely used (Julea et al 2011;Cheng et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Framework for monitoring the conversion of cultivated land to construction land using SAR image time series

Wang

Zhao

Cheng

et al. 2015

Remote Sensing Letters

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The land cover change from cultivated land to construction land is a world issue since the urbanization process is extensively studied around the world. Chengdu, China, is a representative urbanization area, where cloud cover is very high most of the time, restricting the use of visible and near-infrared satellite data. Here, we present a novel framework for land change monitoring based on synthetic aperture radar (SAR) time series, which comprises three key components: (1) construction of pixel-level SAR image time series; (2) spatiotemporal similarity analysis based on morphological-structural characteristics of time series; and (3) iterative binary partition mean square error model analysis to ascertain change nodes. Experimental results showed that the proposed framework could effectively extract the change nodes and change pixels, with correctness of 85.82% and completeness of 84.78%, outperforming the time-series-only (non-spatial) method, as well as traditional classification methods, and the same framework using shorter Landsat TM image time series.

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“…Then, by comparing the level of similarity between the TS of the various pixels and that of known change types, it is possible to determine the type and time of change from the pixels [10]. This method has been successfully applied to the TS of various images, including Synthetic Aperture Radar (SAR) [11], Landsat [12], and Moderate Resolution Imaging Spectroradiometer (MODIS) [13]. Dynamic time warping (DTW) is a similarity measure that exploits the temporal distortions between two TSs.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series

et al. 2020

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High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection.

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Generation of Pixel-Level SAR Image Time Series Using a Locally Adaptive Matching Technique

Cited by 7 publications

References 34 publications

Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

Framework for monitoring the conversion of cultivated land to construction land using SAR image time series

Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series

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