Improving the impervious surface estimation with combined use of optical and SAR remote sensing images

Zhang, Yuanzhi; Zhang, Hongsheng; Lin, Hui

doi:10.1016/j.rse.2013.10.028

Cited by 203 publications

(124 citation statements)

References 42 publications

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“…A difficulty in distinguishing the aquatic and the inland vegetation was also observed. Texture features helped in this respect, as indicated by several previous studies [35,[56][57][58]. Crops were successfully separated from natural vegetation using shape features, which can be easily explained as the cultivated land has more regular patterns and shape than natural vegetation areas.…”

Section: Discussionmentioning

confidence: 66%

“…S1 data first had to split to the study site extend, de-burst the sub-swaths and apply the precise orbit file to offer the highest geometric precision. A Refined Lee 7 × 7 speckle filter was applied after suggestions [35] and the σ 0 outputs were terrain corrected using SNAP's "Range Doppler Terrain Correction" algorithm with the SRTM 1 arc-sec DEM. H-Alpha (H-a) decomposition [41] was included, allowing for entropy and alpha derivatives to be extracted from the data.…”

Section: Pre-processingmentioning

confidence: 99%

“…In this study, four statistical indicators of texture information, namely homogeneity (HOM), dissimilarity (DIS), entropy (ENT) and angular second moment (ASM), were implemented ( Figure 4). These indicators were selected as effective indicators for the texture description of different land cover types [35]. Texture measures are influenced by window size since the scale of the spatial patterns measured, is dependent on window size [45].…”

Section: Analysis Of Texture Featuresmentioning

confidence: 99%

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Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

2017

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Abstract:This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with the Support Vector Machines (SVMs) machine learning classifier for mapping land use and land cover (LULC) with emphasis on wetlands. In this context, the added value of spectral information derived from the Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Grey Level Co-occurrence Matrix (GLCM) to the classification accuracy was also evaluated. As a case study, the National Park of Koronia and Volvi Lakes (NPKV) located in Greece was selected. LULC accuracy assessment was based on the computation of the classification error statistics and kappa coefficient. Findings of our study exemplified the appropriateness of the spatial and spectral resolution of Sentinel data in obtaining a rapid and cost-effective LULC cartography, and for wetlands in particular. The most accurate classification results were obtained when the additional spectral information was included to assist the classification implementation, increasing overall accuracy from 90.83% to 93.85% and kappa from 0.894 to 0.928. A post-classification correction (PCC) using knowledge-based logic rules further improved the overall accuracy to 94.82% and kappa to 0.936. This study provides further supporting evidence on the suitability of the Sentinels 1 and 2 data for improving our ability to map a complex area containing wetland and non-wetland LULC classes.

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Section: Discussionmentioning

confidence: 66%

Section: Pre-processingmentioning

confidence: 99%

Section: Analysis Of Texture Featuresmentioning

confidence: 99%

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Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

2017

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“…In our previous study, we analyzed the optimal setting of T and m by combining optical and SAR data. This study followed the recommendation that T should be bigger than 20 and m should be determined by Equation (5), where M is the total number of features for each sample [37]. In particular, RoF is different from RF in its technical details, although both of them construct a set of decision trees to form a forest with a certain process of randomness.…”

Section: Mangrove Species Classification Using Rotation Forestmentioning

confidence: 99%

Potential of Combining Optical and Dual Polarimetric SAR Data for Improving Mangrove Species Discrimination Using Rotation Forest

et al. 2018

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Classification of mangrove species using satellite images is important for investigating the spatial distribution of mangroves at community and species levels on local, regional and global scales. Hence, studies of mangrove deforestation and reforestation are imperative to support the conservation of mangrove forests. However, accurate discrimination of mangrove species remains challenging due to many factors such as data resolution, species number and spectral confusion between species. In this study, three different combinations of datasets were designed from Worldview-3 and Radarsat-2 data to classify four mangrove species, Kandelia obovate (KO), Avicennia marina (AM), Acanthus ilicifolius (AI) and Aegiceras corniculatum (AC). Then, the Rotation Forest (RoF) method was employed to classify the four mangrove species. Results indicated the benefits of dual polarimetric SAR data with an improvement of accuracy by 2-3%, which can be useful for more accurate large-scale mapping of mangrove species. Moreover, the difficulty of classifying different mangrove species, in order of increasing difficulty, was identified as KO < AM < AI < AC. Dual polarimetric SAR data are recognized to improve the classification of AI and AC species. Although this improvement is not remarkable, it is consistent for all three methods. The improvement can be particularly important for large-scale mapping of mangrove forest at the species level. These findings also provide useful guidance for future studies using multi-source satellite data for mangrove monitoring and conservation.

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“…Integration with other resolution optical remote sensing data with more optical band information, synthetic aperture radar (SAR) data with information in the microwave band, and urban digital surface models (DSM) with height information have potential in urban land cover classification [49][50][51]. Integration with information from more optical bands, such as the near-infrared band, can produce powerful remote sensing indices.…”

mentioning

confidence: 99%

DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image

Huang

Chen

et al. 2018

Remote Sensing

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Implying the prosperity and development of the city, impervious surface area (ISA) is playing an increasingly important role in ecological processes, microclimate, material and energy flows, and urban flood. The free sub-meter resolution Google Earth image, which is integrated by several high spatial resolution data, appears to have potential for high-resolution ISA extraction, where present study is rare and performances remain to be improved. Due to the high spatial and spectral variation of the urban environment as well as confusion between ISA and soil, the accurate delineating of ISA with traditional (direct) methods can be costly and time-consuming, which is in a word resource-intensive. However, this paper presents a novel indirect ISA extraction conceptual model and a new detecting and masking background land cover (DMBLC) approach that: uses a freely available, high-resolution dataset; requires a reduced set of training samples; and consists of relatively simple, common, and feasible image processing steps. The key characteristic of DMBLC is to detect the background of ISA (vegetation, soil, and water) accurately and obtain the ISA by masking the background. The approach relies on background detection to avoid the predicaments of direct ISA extraction. Water can be directly gained by water body vector data, in DMBLC; mixture tuned matched filtering (MTMF) is exploited to detect vegetation and soil, image segmentation is used to mitigate the spectral variation problem within the same land cover, and segment rectangularity reduces the confusion between ISA and soil. From experiments in a core area of Fuzhou, China, the DMBLC approach reached high performance and outperformed the powerful traditional support vector machines (SVM) method (overall accuracy of 94.45% and Kappa coefficient of 0.8885, compared to 86.44% and 0.7329, respectively). From the comparison of different levels of complexity within the inner processing steps, it is confirmed that the DMBLC approach is a powerful and flexible changed framework for indirect ISA extraction, which can be improved by using more advanced inner methods.

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Improving the impervious surface estimation with combined use of optical and SAR remote sensing images

Cited by 203 publications

References 42 publications

Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

Co-Orbital Sentinel 1 and 2 for LULC Mapping with Emphasis on Wetlands in a Mediterranean Setting Based on Machine Learning

Potential of Combining Optical and Dual Polarimetric SAR Data for Improving Mangrove Species Discrimination Using Rotation Forest

DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image

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