scholarly journals Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

2018 ◽  
Vol 10 (5) ◽  
pp. 755 ◽  
Author(s):  
Wei Jiang ◽  
Guojin He ◽  
Tengfei Long ◽  
Yuan Ni ◽  
Huichan Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Water ◽  
Multilayer Perceptron ◽  
Satellite Images ◽  
Water Extraction ◽  
Landsat 8 ◽  
Landsat 8 Oli

Surface Water Extraction From Landsat 8 OLI Imagery Using the LBV Transformation

2017 ◽  
Vol 10 (10) ◽  
pp. 4417-4429 ◽  
Author(s):  
Tianyuan Zhang ◽  
Huazhong Ren ◽  
Qiming Qin ◽  
Chengye Zhang ◽  
Yuanheng Sun
Keyword(s):  
Surface Water ◽  
Water Extraction ◽  
Landsat 8 ◽  
Landsat 8 Oli

scholarly journals Subpixel Surface Water Extraction (SSWE) Using Landsat 8 OLI Data

Water ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 653 ◽  
Author(s):  
Longhai Xiong ◽  
Ruru Deng ◽  
Jun Li ◽  
Xulong Liu ◽  
Yan Qin ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Extraction ◽  
Landsat 8 ◽  
Landsat 8 Oli

scholarly journals Estimation of PMx Concentrations from Landsat 8 OLI Images Based on a Multilayer Perceptron Neural Network

2019 ◽  
Vol 11 (6) ◽  
pp. 646 ◽  
Author(s):  
Bo Zhang ◽  
Meng Zhang ◽  
Jian Kang ◽  
Danfeng Hong ◽  
Jian Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Multilayer Perceptron ◽  
Google Earth ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remote Sensing Images ◽  
Pm10 And Pm2.5

The estimation of PMx (incl. PM10 and PM2.5) concentrations using satellite observations is of great significance for detecting environmental issues in many urban areas of north China. Recently, aerosol optical depth (AOD) data have been being used to estimate the PMx concentrations by implementing linear and/or nonlinear regression analysis methods. However, a lot of relevant research based on AOD published so far have demonstrated some limitations in estimating the spatial distribution of PMx concentrations with respect to estimation accuracy and spatial resolution. In this research, the Google Earth Engine (GEE) platform is employed to obtain the band reflectance (BR) data of a large number of Landsat 8 Operational Land Imager (OLI) remote sensing images. Combined with the meteorological, time parameter and the latitude and longitude zone (LLZ) method proposed in this article, a new BR (band reflectance)-PMx (incl. PM10 and PM2.5) model based on a multilayer perceptron neural network is constructed for the estimation of PMx concentrations directly from Landsat 8 OLI remote sensing images. This research used Beijing, China as the test area and the conducted experiments demonstrated that the BR-PMx model achieved satisfactory performances for the PMx-concentration estimations. The coefficient of determination (R2) of the BR-PM2.5 and BR-PM10 models reached 0.795 and 0.773, respectively, and the root mean square error (RMSE) reached 20.09 μg/m3 and 31.27 μg/m3. Meanwhile, the estimation results have been compared with the results calculated by Kriging interpolation at the same time point, and the spatial distribution is consistent. Therefore, it can be concluded that the proposed BR-PMx model provides a new promising method for acquiring accurate PMx concentrations for various cities of China.

Analysis of Landsat-8 OLI imagery for land surface water mapping

2014 ◽  
Vol 5 (7) ◽  
pp. 672-681 ◽  
Author(s):  
Zhiqiang Du ◽  
Wenbo Li ◽  
Dongbo Zhou ◽  
Liqiao Tian ◽  
Feng Ling ◽  
...  
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Land Surface Water ◽  
Water Mapping

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery

2018 ◽  
Vol 68 ◽  
pp. 73-91 ◽  
Author(s):  
Xiaobiao Wang ◽  
Shunping Xie ◽  
Xueliang Zhang ◽  
Cheng Chen ◽  
Hao Guo ◽  
...  
Keyword(s):  
Surface Water ◽  
Landsat 8 ◽  
Automated Extraction ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Multi Band

scholarly journals Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2580 ◽  
Author(s):  
Tri Acharya ◽  
Anoj Subedi ◽  
Dong Lee
Keyword(s):  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pure Water ◽  
Confusion Matrix ◽  
Threshold Value ◽  
Environmental Noise ◽  
Water Extraction ◽  
Landsat 8 ◽  
Water Indices

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

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