LAND-COVER CLASSIFICATION USING FREELY AVAILABLE MULTITEMPORAL SAR DATA (WORK IN PROGRESS)

Abstract. Synthetic Aperture Radar (SAR) images are a valuable tool for wetlands monitoring since they are able to detect water below the vegetation. Furthermore, SAR images can be acquired regardless of the weather conditions. The monitoring and study of wetlands have become increasingly important due to the social and ecological benefits they provide and the constant pressures they are subject to. The Sentinel-1 mission from the European Space Agency enables the possibility of having free access to multitemporal SAR data. This study aims to investigate the use of multitemporal Sentinel-1 data for wetlands land-cover classification. To perform this assessment, we acquired 76 Sentinel-1 images from a portion of the Lower Delta of the Paraná River, and considering different seasons, texture measurements, and polarization, 30 datasets were created. For each dataset, a Random Forest classifier was trained. Our experiments show that datasets that included the winter dates achieved kappa index values (κ) higher than 0.8. Including textures measurements showed improvements in the classifications: for the summer datasets, the κ increased more than 14%, whereas, for Winter datasets in the VH and Dual polarization, the improvements were lower than 4%. Our results suggest that for the analyzed land-cover classes, winter is the most informative season. Moreover, for Summer datasets, the textures measurements provide complementary information.

Download Full-text

Detection of Land Subsidence due to Excessive Groundwater Use Varying with Different Land Cover Types in Quetta valley, Pakistan Using ESA-Sentinel Satellite Data

10.5194/nhess-2017-234 ◽

2017 ◽

Cited By ~ 4

Author(s):

Waqas Ahmad ◽

Minha Choi ◽

Soohyun Kim ◽

Dongkyun Kim

Keyword(s):

Land Cover ◽

Satellite Data ◽

Land Subsidence ◽

European Space Agency ◽

Multispectral Images ◽

Sar Images ◽

Entire Study ◽

Space Agency ◽

Quetta Valley ◽

Entire Study Area

Abstract. In this study, we show that land subsidence in Quetta valley, Pakistan is caused by exploitation of groundwater based on the analysis of European Space Agency (ESA) Sentinel satellite data. For this, we performed interferometry analysis using twenty nine Sentinel-1 SAR images to obtain twenty eight interferograms of land subsidence for the period between 16 Oct 2014 and 06 Oct 2016. Then, the land subsidence was compared with the land cover of the study area that was derived from Sentienl-2 multispectral images. The results reveal that, for the period of two years, the entire study area experienced highly uneven land subsidence with its magnitude ranging between 10 mm to 280 mm. The spatial pattern of land subsidence showed a high correlation with that of land covers. While urban and cultivated area with high groundwater extraction showed great amount of land subsidence, barren and seasonally cultivated area did not show as much land subsidence.

Download Full-text

Use of Sentinel-1 Data in Flood Mapping in the Buna River Area

International Journal of Environment and Climate Change ◽

10.9734/ijecc/2020/v10i1030257 ◽

2020 ◽

pp. 147-156

Author(s):

Freskida Abazaj ◽

Gëzim Hasko

Keyword(s):

European Space Agency ◽

Weather Conditions ◽

Synthetic Aperture ◽

Flood Mapping ◽

Sar Images ◽

Space Agency ◽

Optical Images ◽

River Region ◽

Speckle Filtering ◽

Flooded Areas

Floods are one of the disasters that cause many human lives and property. In Albania, most floods are associated with periods of heavy rainfall. In recent years, Synthetic Aperture Radar (SAR) sensors, which provide reliable data in all weather conditions and day and night, have been favored because they eliminate the limitations of optical images. In this study, a flood occurred in the Buna River region in March 2018, was mapped using SAR Sentinel-1 data. The aim of this study is to investigate the potential of flood mapping using SAR images using different methodologies. Sentinel-1A / B SAR images of the study area were obtained from the European Space Agency (ESA). Preprocessing steps, which include trajectory correction, calibration, speckle filtering, and terrain correction, have been applied to the images. RGB composition and the calibrated threshold technique have been applied to SAR images to detect flooded areas and the results are discussed here.

Download Full-text

Land Cover Classification for Polarimetric SAR Images Based on Mixture Models

Remote Sensing ◽

10.3390/rs6053770 ◽

2014 ◽

Vol 6 (5) ◽

pp. 3770-3790 ◽

Cited By ~ 21

Author(s):

Wei Gao ◽

Jian Yang ◽

Wenting Ma

Keyword(s):

Land Cover ◽

Mixture Models ◽

Land Cover Classification ◽

Polarimetric Sar ◽

Sar Images

Download Full-text

Discrimination of glacier facies using multi-temporal SAR data

Journal of Glaciology ◽

10.1017/s0022143000002331 ◽

1998 ◽

Vol 44 (146) ◽

pp. 42-53 ◽

Cited By ~ 59

Author(s):

K. C. Partington

Keyword(s):

European Space Agency ◽

Greenland Ice Sheet ◽

Sensor Calibration ◽

Lower Altitude ◽

Mount Pinatubo ◽

South Central ◽

Space Agency ◽

Wet Snow ◽

Multi Temporal ◽

Sar Data

AbstractGlacier facies from the Greenland ice sheet and the Wrangell-St Elias Mountains, Alaska, are analyzed using multi-temporal synthetic aperture radar (SAR) data from the European Space Agency ERS-1 satellite. Distinct zones and facies are visible in multi-temporal SAR data, including the dry-snow facies, the combined percolation and wet-snow facies, the ice facies, transient melt areas and moraine. In Greenland and south-central Alaska, very similar multi-temporal signatures are evident for the same facies, although these facies are found at lower altitude in West Greenland where the equilibrium line appears to be found at sea level at 71°30?N during the year analyzed (1992-93), probably because of the cooling effect of the eruption of Mount Pinatubo. In Greenland, both the percolation and dry-snow facies are excellent distributed targets for sensor calibration, with backscatter coefficients stable to within 0.2 dB. However, the percolation facies near the top of Mount Wrangell are more complex and less easily delineated than in Greenland, and at high altitude the glacier facies have a multi-temporal signature which depends sensitively on slope orientation.

Download Full-text

Evaluation of speckle filtering and texture analysis methods for land cover classification from SAR images

International Journal of Remote Sensing ◽

10.1080/01431160110036157 ◽

2002 ◽

Vol 23 (9) ◽

pp. 1895-1925 ◽

Cited By ~ 59

Author(s):

A. Ndi Nyoungui ◽

E. Tonye ◽

A. Akono

Keyword(s):

Land Cover ◽

Texture Analysis ◽

Land Cover Classification ◽

Sar Images ◽

Analysis Methods ◽

Speckle Filtering

Download Full-text

Unsupervised Land Cover Classification on SAR Images by Clustering Backscatter Coefficients

Algorithms for Intelligent Systems - Intelligent Systems ◽

10.1007/978-981-16-2248-9_43 ◽

2021 ◽

pp. 445-454

Author(s):

Emily Jenifer ◽

Natarajan Sudha

Keyword(s):

Land Cover ◽

Land Cover Classification ◽

Sar Images

Download Full-text

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs11121461 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1461 ◽

Cited By ~ 18

Author(s):

Husam A. H. Al-Najjar ◽

Bahareh Kalantar ◽

Biswajeet Pradhan ◽

Vahideh Saeidi ◽

Alfian Abdul Halin ◽

...

Keyword(s):

Neural Networks ◽

Land Cover ◽

Convolutional Neural Networks ◽

Land Cover Classification ◽

Training Data ◽

Surface Model ◽

Kappa Index ◽

Diverse Range ◽

Average Accuracy ◽

Uav Images

In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)–digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of ~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants were dense.

Download Full-text