A Method for Classifying Land and Ocean Area by Removing Sentinel-1 Speckle Noise

Han, Hyeon-Gyeong; Lee, Moung-Jin

doi:10.2112/si102-004.1

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…e end of the season is defined as the time during the maturation period when there is a significant decrease in NDVI. Generally, it corresponds to the period during which chlorophyll activity gradually decreases [27]. ese evolutions can be seen in Figure 6 graphs B, C, and F for cereal, double cropping, and fallow.…”

Section: Ndvi Profilesmentioning

confidence: 93%

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Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

Moumni

Lahrouni

2021

Scientifica

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The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. It is a very heterogeneous zone characterized by mixed crops (tree crops with annual crops, same crop with different phenological states during the same agricultural season, crop rotation, etc.). Therefore, such heterogeneity made the crop-type discrimination more complicated. To overcome these challenges, the present work is the first study in this area which used the fusion of high spatiotemporal resolution Sentinel-1 and Sentinel-2 satellite images for land use and land cover mapping. Three machine learning classifier algorithms, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML), were applied to identify and map crop types in irrigated perimeter. In situ observations of the year 2018, for the R3 perimeter of Haouz plain in central Morocco, were used with satellite data of the same year to perform this work. The results showed that combined images acquired in C-band and the optical range improved clearly the crop-type classification performance (overall accuracy = 89%; Kappa = 0.85) compared to the classification results of optical or SAR data alone.

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Section: Ndvi Profilesmentioning

confidence: 93%

“…To reduce the speckle noise in the acquired S1 images, Enhanced Lee Filter [24] was applied. In the literature, there is a large family of filters [25][26][27]; therefore, Enhanced Lee Filter is one of the efficient filters used for SAR imagery in particular for areas with high degree of heterogeneity [28].…”

Section: Data Processingmentioning

confidence: 99%

Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

Moumni

Lahrouni

2021

Scientifica

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“…The GINI coefficient was chosen because it provides an idea of the average total diminution of the impurity of the nodes, and because it was applied to all of the RF decision trees. GINI feature provided us with information on how important the input characteristic was relative to the classification (Argamosa et al, 2018;Descals et al, 2019;Han & Lee, 2020;Mansaray et al, 2020). The sample point was performed to develop the categorization of S1A and S2B with RF.…”

Section: Processingmentioning

confidence: 99%

LAND USE LAND COVER CHANGE MAPPING FROM SENTINEL 1B < 2A IMAGERY USING RANDOM FOREST ALGORITHM IN CÔTE D’IVOIRE

Kouassi,

Qian,

Khan

et al. 2024

Geodesy and Cartography

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Monitoring crop condition, soil properties, and mapping tillage activities can be used to assess land use, forecast crops, monitor seasonal changes, and contribute to the implementation of sustainable development policy. Agricultural maps can provide independent and objective estimates of the extent of crops in a given area or growing season, which can be used to support efforts to ensure food security in vulnerable areas. Satellite data can help detect and classify different types of soil. The evolution of satellite remote sensing technologies has transformed techniques for monitoring the Earth’s surface over the last several decades. The European Space Agency (ESA) and the European Union (EU) created the Copernicus program, which resulted in the European satellites Sentinel-1B (S1B) and Sentinel-2A (S2A), which allow the collection of multi-temporal, spatial, and highly repeatable data, providing an excellent opportunity for the study of land use, land cover, and change. The goal of this study is to map the land cover of Côte d’Ivoire’s West Central Soubre area (5°47′1′′ North, 6°35′38′′ West) between 2014 and 2020. The method is based on a combination of S1B and S2A imagery data, as well as three types of predictors: the biophysical indices Normalized Difference Vegetation Index “(NDVI)”, Modified Normalized Difference Water Index “(MNDWI)”, Normalized Difference Urbanization Index “(NDBI)”, and Normalized Difference Water Index “(NDWI)”, as well as spectral bands (B1, B11, B2, B3, B4, B6, B7, B8) and polarization coefficients VV. For the period 2014–2020, six land classifications have been established: Thick_Forest, Clear_Drill, Urban, Water, Palm_Oil, Bareland, and Cacao_Land. The Random Forest (RF) algorithm with 60 numberOfTrees was the primary categorization approach used in the Google Earth Engine (GEE) platform. The results show that the RF classification performed well, with outOfBagErrorEstimates of 0.0314 and 0.0498 for 2014 and 2020, respectively. The classification accuracy values for the kappa coefficients were above 95%: 96.42% in 2014 and 95.28% in 2020, with an overall accuracy of 96.97% in 2014 and 96 % in 2020. Furthermore, the User Accuracy (UA) and Producer Accuracy (PA) values for the classes were frequently above 80%, with the exception of the Bareland class in 2020, which achieved 79.20%. The backscatter coefficients of the S1B polarization variables had higher GINI significance in 2014: VH (70.80) compared to VH (50.37) in 2020; and VV (57.11) in 2014 compared to VV (46.17) in 2020. Polarization coefficients had higher values than the other spectral and biophysical variables of the three predictor variables. During the study period, the Thick_Forest (35.90% ± 1.17), Palm_Oil (57.59% ± 1.48), and Water (5.90% ± 0.47) classes experienced a regression in area, while the Clear_Drill (16.96% ± 0.80), Urban (2.32% ± 0.29), Bareland (83.54% ± 1.79), and Cacao_Land (35.14% ± 1.16) classes experienced an increase. The approach used is regarded as excellent based on the results obtained.

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“…The enhanced Lee filter has been chosen in this study to minimize the speckle effect on the images without loss of information. According to the literature, it is one of the most used filters to reduce speckle noise in SAR remote sensing data [59,60].…”

Section: Sar Imagerymentioning

confidence: 99%

Argan Tree (Argania spinosa (L.) Skeels) Mapping Based on Multisensor Fusion of Satellite Imagery in Essaouira Province, Morocco

et al. 2021

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Tree species identification and their geospatial distribution mapping are crucial for forest monitoring and management. The satellite-based remote sensing time series of Sentinel missions (Sentinel-1 and Sentinel-2) are a perfect tool to map the type, location, and extent of forest cover over large areas at local or global scale. This study is focused on the geospatial mapping of the endemic argan tree (Argania spinosa (L.) Skeels) and the identification of two other tree species (sandarac gum and olive trees) using optical and synthetic aperture radar (SAR) time series. The objective of the present work is to detect the actual state of forest species trees, more specifically the argan tree, in order to be able to study and analyze forest changes (degradation) and make new strategies to protect this endemic tree. The study was conducted over an area located in Essaouira province, Morocco. The support vector machine (SVM) algorithm was used for the classification of the two types of data. We first classified the optical data for tree species identification and mapping. Second, the SAR time series were used to identify the argan tree and distinguish it from other species. Finally, the two types of satellite images were combined to improve and compare the results of classification with those obtained from single-source data. The overall accuracy (OA) of optical classification reached 86.9% with a kappa coefficient of 0.84 and declined strongly to 37.22% (kappa of 0.29) for SAR classification. The fusion of multisensor data (optical and SAR images) reached an OA of 86.51%. A postclassification was performed to improve the results. The classified images were smoothed, and therefore, the quantitative and qualitative results showed an improvement, in particular for optical classification with a highest OA of 89.78% (kappa coefficient of 0.88). The study confirmed the potential of the multitemporal optical data for accurate forest cover mapping and endemic species identification.

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A Method for Classifying Land and Ocean Area by Removing Sentinel-1 Speckle Noise

Cited by 5 publications

References 13 publications

Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

LAND USE LAND COVER CHANGE MAPPING FROM SENTINEL 1B < 2A IMAGERY USING RANDOM FOREST ALGORITHM IN CÔTE D’IVOIRE

Argan Tree (Argania spinosa (L.) Skeels) Mapping Based on Multisensor Fusion of Satellite Imagery in Essaouira Province, Morocco

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