scholarly journals Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

Water ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 82
Author(s):  
Huaxin Liu ◽  
Qigang Jiang ◽  
Yue Ma ◽  
Qian Yang ◽  
Pengfei Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Classification Scheme ◽  
Land Cover Classification ◽  
Support Vector ◽  
Wetland Classification ◽  
Positive Contribution ◽  
Object Based ◽  
Extreme Gradient Boosting ◽  
Sentinel 2

The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources.

scholarly journals INTEGRATION OF MULTITEMPORAL SENTINEL-1 AND SENTINEL-2 IMAGERY FOR LAND-COVER CLASSIFICATION USING MACHINE LEARNING METHODS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 91-98 ◽  
Author(s):  
D. Dobrinić ◽  
D. Medak ◽  
M. Gašparović
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Computational Time ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Sar Imagery ◽  
Sentinel 2

Abstract. Using space-borne remote sensing data is widely used for land-cover classification (LCC) due to its ability to provide a big amount of data with a regular temporal revisit time. In recent years, optical and synthetic aperture radar (SAR) imagery have become available for free, and their integration in time series have improved LCC. This research evaluates the classification accuracy using multitemporal (MT) Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Pixel-based LCC is made for S1 and S2 imagery, and for a combination of both datasets with Random Forest (RF) and Extreme Gradient Boosting (XGBoost; XGB). The extent of the study area, is located in the south-east of France, in Lyon. Regardless of LCC using single-date or MT data, the highest classification results were achieved with integrated S1 and S2 imagery and XGB method, whereas overall accuracy (OA) and Kappa coefficient (Kappa) increased from 85.51% to 91.09%, and from 0.81 to 0.88, respectively. Furthermore, the integration of MT imagery significantly improved the classification of urban areas and reduced misclassification between forest and low vegetation. In this paper, in terms of the pixel-based classification, XGB produced slightly better results than RF, and outperformed it in terms of computational time. This research improved LCC with integration of radar and optical MT imagery, which can be useful for areas hampered by a frequent cloud cover. Future work should use the aforementioned data for specific applications in remote sensing, as well as evaluate the classification performance with different approaches, such as neural networks or deep learning.

scholarly journals ASSESSMENT OF COMBINING CONVOLUTIONAL NEURAL NETWORKS AND OBJECT BASED IMAGE ANALYSIS TO LAND COVER CLASSIFICATION USING SENTINEL 2 SATELLITE IMAGERY (TENES REGION, ALGERIA)

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 383-389
Author(s):  
N. Zaabar ◽  
S. Niculescu ◽  
M. K. Mihoubi
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Land Cover ◽  
Image Classification ◽  
Support Vector ◽  
Object Based Image Analysis ◽  
Sensing Applications ◽  
Object Based ◽  
Land Cover Maps ◽  
Sentinel 2

Abstract. Land cover maps can provide valuable information for various applications, such as territorial monitoring, environmental protection, urban planning and climate change prevention. In this purpose, remote sensing based on image classification approaches undergoing a high revolution can be dedicated to land cover mapping tasks. Similarly, deep learning models are considerably applied in remote sensing applications; which can automatically learn features from large amounts of data. Prevalently, the Convolutional Neural Network (CNN), have been increasingly performed in image classification. The aim of this study is to apply a new approach to analyse land cover, and extract its features. Experiments carried out on a coastal town located in north-western Algeria (Ténès region). The study area is chosen because of its importance as a part of the national strategy to combat natural hazards, specifically floods. As well as, a simple CNN model with two hidden layers was constructed, combined with an Object-Based Image Analysis (OBIA). In this regard, a Sentinel-2 image was used, to perform the classification, using spectral index combinations. Furthermore, to compare the performance of the proposed approach, an OBIA based on machines learning algorithms mainly Random Forest (RF) and Support Vector Machine (SVM), was provided. Results of accuracy assessment of classification showed good values in terms of Overall accuracy and Kappa Index, which reach to 93.1% and 0.91, respectively. As a comparison, CNN-OBIA approach outperformed OBIA based on RF algorithm. Therefore, Final land cover maps can be used as a support tool in regional and national decisions.

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

scholarly journals Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

2018 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Wetland Classification ◽  
Significant Information ◽  
Data Set ◽  
Object Based ◽  
Sentinel 2

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

scholarly journals LAND USE AND LAND COVER MAPPING – A CASE STUDY OF AHMEDABAD DISTRICT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 189-193
Author(s):  
D. Rawal ◽  
A. Chhabra ◽  
M. Pandya ◽  
A. Vyas
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Spectral Reflectance ◽  
Land Cover Classification ◽  
Land Cover Mapping ◽  
Classification Algorithms ◽  
Classification Technique ◽  
Object Based

Abstract. Land cover mapping using remote-sensing imagery has attracted significant attention in recent years. Classification of land use and land cover is an advantage of remote sensing technology which provides all information about land surface. Numerous studies have investigated land cover classification using different broad array of sensors, resolution, feature selection, classifiers, Classification Techniques and other features of interest from over the past decade. One, Pixel based image classification technique is widely used in the world which works on their per pixel spectral reflectance. Classification algorithms such as parallelepiped, minimum distance, maximum likelihood, Mahalanobis distance are some of the classification algorithms used in this technique. Other, Object based image classification is one of the most adapted land cover classification technique in recent time which also considers other parameters such as shape, colour, smoothness, compactness etc. apart from the spectral reflectance of single pixel.At present, there is a possibility of getting the more accurate information about the land cover classification by using latest technology, recent and relevant algorithms according to our study. In this study a combination of pixel-by-pixel image classification and object based image classification is done using different platforms like ArcGIS and e-cognition, respectively. The aim of the study is to analyze LULC pattern using satellite imagery and GIS for the Ahmedabad district in the state of Gujarat, India using a LISS-IV imagery acquired from January to April, 2017. The over-all accuracy of the classified map is 84.48% with Producer’s and User’s accuracy as 89.26% and 84.47% respectively. Kappa statistics for the classified map are calculated as 0.84. This classified map at 1:10,000 scale generated using recent available high resolution space borne data is a valuable input for various research studies over the study area and also provide useful information to town planners and civic authorities. The developed technique can be replicated for generating such LULC maps for other study areas as well.

scholarly journals Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel

2021 ◽  
Vol 13 (17) ◽  
pp. 3488
Author(s):  
Keren Goldberg ◽  
Ittai Herrmann ◽  
Uri Hochberg ◽  
Offer Rozenstein
Keyword(s):  
Time Series ◽  
Mean Shift ◽  
Vegetation Indices ◽  
Gradient Boosting ◽  
Support Vector ◽  
Object Based ◽  
Accuracy Measure ◽  
Extreme Gradient Boosting ◽  
Accuracy Measures ◽  
Sentinel 2

The overarching aim of this research was to develop a method for deriving crop maps from a time series of Sentinel-2 images between 2017 and 2018 to address global challenges in agriculture and food security. This study is the first step towards improving crop mapping based on phenological features retrieved from an object-based time series on a national scale. Five main crops in Israel were classified: wheat, barley, cotton, carrot, and chickpea. To optimize the object-based classification process, different characteristics and inputs of the mean shift segmentation algorithm were tested, including vegetation indices, three-band combinations, and high/low emphasis on the spatial and spectral characteristics. Four known vegetation indices (VIs)-based time series were tested. Additionally, we compared two widely used machine learning methods for crop classification, support vector machine (SVM) and random forest (RF), in addition to a newer classifier, extreme gradient boosting (XGBoost). Lastly, we examined two accuracy measures—overall accuracy (OA) and area under the curve (AUC)—in order to optimally estimate the accuracy in the case of imbalanced class representation. Mean shift best performed when emphasizing both the spectral and spatial characteristics while using the green, red, and near-infrared (NIR) bands as input. Both accuracy measures showed that RF and XGBoost classified different types of crops with significantly greater success than achieved by SVM. Nevertheless, AUC was better able to represent the significant differences between the classification algorithms than OA was. None of the VIs showed a significantly higher contribution to the classification. However, normalized difference infrared index (NDII) with XGBoost classifier showed the highest AUC results (88%). This study demonstrates that the short-wave infrared (SWIR) band with XGBoost improves crop type classification results. Furthermore, the study emphasizes the importance of addressing imbalanced classification datasets by using a proper accuracy measure. Since object-based classification and phenological features derived from a VI-based time series are widely used to produce crop maps, the current study is also relevant for operational agricultural management and informatics at large scales.

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