Single-Polarized SAR Classification Based on a Multi-Temporal Image Stack

Lin, Keng-Fan; Perissin, Daniele

doi:10.3390/rs10071087

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…In order to show the advantages of the proposed method, the accuracy of the classification results of the proposed decision tree is compared with that of multi-temporal classification. It has become one of the important methods of ground object classification in the field of SAR remote sensing to make use of multiple temporal SAR images for pseudocolor synthesis and supervised classification by color difference of pseudocolor images (Lin and Perissin, 2018;Wang et al, 2018;. 16 Sentinel-1A GRD data and 16 Sentinel-1A SLC data with VH polarization were selected and the imaging time of GRD data and SLC data were consistent.…”

Section: Multiple Temporal Classificationmentioning

confidence: 99%

Classification and Evolutionary Analysis of Yellow River Delta Wetlands Using Decision Tree Based on Time Series SAR Backscattering Coefficient and Coherence

Wang

Liu

et al. 2022

Front. Mar. Sci.

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In recent years, the Yellow River Delta has been affected by invasive species Spartina alterniflora (S. alterniflora), resulting in a fragile ecological environment. It is of great significance to monitor the ground object types in the Yellow River Delta wetlands. The classification accuracy based on Synthetic Aperture Radar (SAR) backscattering coefficient is limited by the small difference between some ground objects. To solve this problem, a decision tree classification method for extracting the ground object types in wetland combined time series SAR backscattering and coherence characteristics was proposed. The Yellow River Delta was taken as the study area and the 112 Sentinel-1A GRD data with VV/VH dual-polarization and 64 Sentinel-1A SLC data with VH polarization were used. The decision tree method was established, based on the annual mean VH and VV backscattering characteristics, the new constructed radar backscattering indices, and the annual mean VH coherence characteristics were suitable for extracting the wetlands in the Yellow River Delta. Then the classification results in the Yellow River Delta wetlands from 2018 to 2021 were obtained using the new method proposed in this paper. The results show that the overall accuracy and Kappa coefficient of the proposed method w5ere 89.504% and 0.860, which were 9.992% and 0.127 higher than multi-temporal classification by Support Vector Machine classifier. Compared with the decision tree without coherence, the overall accuracy and Kappa coefficient were improved by 8.854% and 0.108. The spatial distributions of wetland types in the Yellow River Delta from 2018 to 2021 were obtained using the constructed decision tree. The spatio-temporal evolution analysis was conducted. The results showed that the area of S. alterniflora decreased significantly in 2020 but it increased to the area of 2018 in 2021. In addition, S. alterniflora seriously affected the living space of Phragmites australis (P. australis) and in 4 years, 10.485 km2 living space of P. australis was occupied by S. alterniflora. The proposed method can provide a theoretical basis for higher accuracy SAR wetland classification and the monitoring results can provide an effective reference for local wetland protection.

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Section: Multiple Temporal Classificationmentioning

confidence: 99%

Classification and Evolutionary Analysis of Yellow River Delta Wetlands Using Decision Tree Based on Time Series SAR Backscattering Coefficient and Coherence

Wang

Liu

et al. 2022

Front. Mar. Sci.

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“…Whereas homogeneous fields such as vegetation-covers have textural homogeneity, urban features show varying textural variations. There are several textural feature extraction methods for S1-SAR including; the Neighbourhood Correlation Images (NCI) and Object Correlation Images (OCI) [27] and the Grey-Level Occurrence Matrix (GLCM) method [28]. GLCM is generally is well-known in literature and frequently used for bench matching studies because it is more efficient in reporting correlation degree between pixel pairs in order to ascertain intensity, uniformity, homogeneity and energy among others.…”

Section: Textural Feature Extractionmentioning

confidence: 99%

Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification

Abudu¹,

Parvin²,

Andogah³

2020

IJSER

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Conventional approaches for urban land use land cover classification and quantification of land use changes have often relied on the ground surveys and urban censuses of urban surface properties. Advent of Remote Sensing technology supporting metric to centimetric spatial resolutions with simultaneous wide coverage, significantly reduced huge operational costs previously encountered using ground surveys. Weather, sensor’s spatial resolution and the complex compositions of urban areas comprising concrete, metallic, water, bare- and vegetation-covers, limits Remote Sensing ability to accurately discriminate urban features. The launch of Sentinel-1 Synthetic Aperture Radar, which operates at metric resolution and microwave frequencies evades the weather limitations and has been reported to accurately quantify urban compositions. This paper assessed the feasibility of Sentinel-1 SAR data for urban land use land cover classification by reviewing research papers that utilised these data. The review found that since 2014, 11 studies have specifically utilised the datasets.

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“…relationship of image pixel values with their spatial distribution in the landscape by describing the frequency with which different combinations of brightness values occur for each pixel in its predefined neighborhood (Hall-Beyer, 2017). Including GLCM textures measurement derived from SAR images had shown an improvement in vegetation discrimination when using single-date SAR data (Treitz et al, 2000;Arzandeh and Wang, 2002;Krishna et al, 2018;Panuju et al 2019) or single-polarization SAR data (Kurvonen et al, 1999;Lin et al, 2018). SAR texture measurements may change according to the phenological vegetation state.…”

Section: Introductionmentioning

confidence: 99%

Cover classifications in wetlands using Sentinel-1 data (Band C): a case study in the Parana river delta, Argentina

Rajngewerc

Grimson

Bali³

et al. 2022

Rev. Teledetec.

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With the launch of the Sentinel-1 mission, for the first time, multitemporal and dual-polarization C-band SAR data with a short revisit time is freely available. How can we use this data to generate accurate vegetation cover maps on a local scale? Our main objective was to assess the use of multitemporal C-Band Sentinel-1 data to generate wetland vegetation maps. We considered a portion of the Lower Delta of the Paraná River wetland (Argentina). Seventy-four images were acquired and 90 datasets were created with them, each one addressing a combination of seasons (spring, autumn, winter, summer, complete set), polarization (VV, HV, both), and texture measures (included or not). For each dataset, a Random Forest classifier was trained. Then, the kappa index values (κ) obtained by the 90 classifications made were compared. Considering the datasets formed by the intensity values, for the winter dates the achieved kappa index values (κ) were higher than 0.8, while all summer datasets achieved κ up to 0.76. Including feature textures based on the GLCM showed improvements in the classifications: for the summer datasets, the κ improvements were between 9% and 22% and for winter datasets improvements were up to 15%. Our results suggest that for the analyzed context, winter is the most informative season. Moreover, for dates associated with high biomass, the textures provide complementary information.

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Single-Polarized SAR Classification Based on a Multi-Temporal Image Stack

Cited by 8 publications

References 29 publications

Classification and Evolutionary Analysis of Yellow River Delta Wetlands Using Decision Tree Based on Time Series SAR Backscattering Coefficient and Coherence

Classification and Evolutionary Analysis of Yellow River Delta Wetlands Using Decision Tree Based on Time Series SAR Backscattering Coefficient and Coherence

Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification

Cover classifications in wetlands using Sentinel-1 data (Band C): a case study in the Parana river delta, Argentina

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