scholarly journals Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water

2018 ◽  
Vol 10 (11) ◽  
pp. 1704 ◽  
Author(s):  
Wei Wu ◽  
Qiangzi Li ◽  
Yuan Zhang ◽  
Xin Du ◽  
Hongyan Wang
Keyword(s):  
Surface Water ◽  
High Resolution ◽  
Urban Surface ◽  
Mapping Technique ◽  
Support Vector ◽  
Svm Classifier ◽  
P Value ◽  
Urban Water ◽  
Water Index ◽  
Water Mapping

Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 2.28%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy.

scholarly journals Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

2020 ◽  
Vol 9 (2) ◽  
pp. 109 ◽  
Author(s):  
Bo Cheng ◽  
Shiai Cui ◽  
Xiaoxiao Ma ◽  
Chenbin Liang
Keyword(s):  
High Resolution ◽  
Extraction Method ◽  
Texture Features ◽  
Decomposition Methods ◽  
Projection Matrix ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Building Area ◽  
Adaptive Neighborhood

Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.

scholarly journals Volatile organic compound breath signatures of children with cystic fibrosis by real-time SESI-HRMS

2020 ◽  
Vol 6 (1) ◽  
pp. 00171-2019 ◽  
Author(s):  
Ronja Weber ◽  
Naemi Haas ◽  
Astghik Baghdasaryan ◽  
Tobias Bruderer ◽  
Demet Inci ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
High Resolution ◽  
Organic Compound ◽  
Real Time ◽  
Volatile Organic Compound ◽  
Lung Damage ◽  
Support Vector ◽  
P Value ◽  
Large Set ◽  
Volatile Organic

Early pulmonary infection and inflammation result in irreversible lung damage and are major contributors to cystic fibrosis (CF)-related morbidity. An easy to apply and noninvasive assessment for the timely detection of disease-associated complications would be of high value. We aimed to detect volatile organic compound (VOC) breath signatures of children with CF by real-time secondary electrospray ionisation high-resolution mass spectrometry (SESI-HRMS).A total of 101 children, aged 4–18 years (CF=52; healthy controls=49) and comparable for sex, body mass index and lung function were included in this prospective cross-sectional study. Exhaled air was analysed by a SESI-source linked to a high-resolution time-of-flight mass spectrometer. Mass spectra ranging from m/z 50 to 500 were recorded.Out of 3468 m/z features, 171 were significantly different in children with CF (false discovery rate adjusted p-value of 0.05). The predictive ability (CF versus healthy) was assessed by using a support-vector machine classifier and showed an average accuracy (repeated cross-validation) of 72.1% (sensitivity of 77.2% and specificity of 67.7%).This is the first study to assess entire breath profiles of children with SESI-HRMS and to extract sets of VOCs that are associated with CF. We have detected a large set of exhaled molecules that are potentially related to CF, indicating that the molecular breath of children with CF is diverse and informative.

Classification of High Resolution Sar Imagery Using Local Indicators of Spatial Association

2013 ◽  
Vol 333-335 ◽  
pp. 1080-1084
Author(s):  
Zhang Fei ◽  
Ye Xi
Keyword(s):  
Support Vector Machines ◽  
High Resolution ◽  
Classification Accuracy ◽  
Spatial Association ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Good Classification ◽  
Sar Imagery

In this paper, we will propose a novel classification method of high-resolution SAR using local autocorrelation and Support Vector Machines (SVM) classifier. The commonly applied spatial autocorrelation indexes, called Moran's Index; Geary's Index, Getis's Index, will be used to depict the feature of the land-cover. Then, the SVM based on these indexes will be applied as the high-resolution SAR classifier. A Cosmo-SkyMed scene in ChengDu city, China is used for our experiment. It is shown that the method proposed can lead to good classification accuracy.

scholarly journals Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2

2018 ◽  
Vol 10 (10) ◽  
pp. 1643 ◽  
Author(s):  
Zifeng Wang ◽  
Junguo Liu ◽  
Jinbao Li ◽  
David Zhang
Keyword(s):  
Spatial Resolution ◽  
Spectral Band ◽  
Support Vector ◽  
High Threshold ◽  
Commission Error ◽  
Produce Water ◽  
Water Index ◽  
Water Mapping ◽  
Index Methods ◽  
Sentinel 2

Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.

scholarly journals Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2769 ◽  
Author(s):  
Tri Dev Acharya ◽  
Anoj Subedi ◽  
Dong Ha Lee
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Recursive Partitioning ◽  
Learning Algorithms ◽  
High Elevation ◽  
Machine Learning Algorithms ◽  
Water Extraction ◽  
Support Vector ◽  
Landsat 8 ◽  
Water Index

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance.

scholarly journals Fusion of High- and Medium-Resolution Optical Remote Sensing Imagery and GlobeLand30 Products for the Automated Detection of Intra-Urban Surface Water

2020 ◽  
Vol 12 (24) ◽  
pp. 4037
Author(s):  
Zhi Li ◽  
Xiaomei Yang
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Automated Detection ◽  
Urban Surface ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Model Based

Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of IUSW because of their characteristics of high resolution, large width, and high frequency. The lack of spectral information in high-resolution remote sensing data, however, has led to the IUSW misclassification problem, which is difficult to fully solve by relying only on spatial features. In addition, with an increasing abundance of water products, it is equally important to explore methods for using water products to further enhance the automatic acquisition of IUSW. In this study, we developed an automated urban surface-water area extraction method (AUSWAEM) to obtain accurate IUSW by fusing GaoFen-1 (GF-1) images, Landsat-8 Operational Land Imager (OLI) images, and GlobeLand30 products. First, we derived morphological large-area/small-area water indices to increase the salience of IUSW features. Then, we applied an adaptive segmentation model based on the GlobeLand30 product to obtain the initial results of IUSW. Finally, we constructed a decision-level fusion model based on expert knowledge to eliminate the problem of misclassification resulting from insufficient information from high-resolution remote sensing spectra and obtained the final IUSW results. We used a three-case study in China (i.e., Tianjin, Shanghai, and Guangzhou) to validate this method based on remotely sensed images, such as those from GF-1 and Landsat-8 OLI. We performed a comparative analysis of the results from the proposed method and the results from the normalized differential water index, with average kappa coefficients of 0.91 and 0.55, respectively, which indicated that the AUSWAEM improved the average kappa coefficient by 0.36 and obtained accurate spatial patterns of IUSW. Furthermore, the AUSWAEM displayed more stable and robust performance under different environmental conditions. Therefore, the AUSWAEM is a promising technique for extracting IUSW with more accurate and automated detection performance.

scholarly journals A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI

2013 ◽  
Vol 5 (11) ◽  
pp. 5530-5549 ◽  
Author(s):  
Wenbo Li ◽  
Zhiqiang Du ◽  
Feng Ling ◽  
Dongbo Zhou ◽  
Hailei Wang ◽  
...  
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Water Index ◽  
Land Surface Water ◽  
Normalized Difference Water Index ◽  
Water Mapping
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