Incorporation of textural information with SAR and optical imagery for improved land cover mapping

Muthukumarasamy, Iyyappan; Shanmugam, Ramakrishnan S.; Usha, Tune

doi:10.1007/s12665-019-8654-9

Cited by 11 publications

(6 citation statements)

References 41 publications

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“…The Sentinel-1A GRD images in GEE were processed by the Sentinel-1 toolbox, including thermal noise removal, radiometric correction, terrain correction using the digital elevation model (DEM), and conversion of the backscattering coefficient to decibels (dB). After the successful launch of the Sentinel-2B, the temporal resolution of Sentinel-2 images was increased to five days, and both satellites carry a multispectral instrument (MSI) with an orbital width of 290 km [31]. The MSI images acquired by this satellite possess 13 bands covering visible, near-infrared, and short-wave infrared with spatial resolutions of 10 m, 20 m, and 60 m, respectively (Table 1).…”

Section: Sentinel Datamentioning

confidence: 99%

Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

Weinan

Yi-lin

et al. 2022

Remote Sensing

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The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat.

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Section: Sentinel Datamentioning

confidence: 99%

Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

Weinan

Yi-lin

et al. 2022

Remote Sensing

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“…is chapter combines the two algorithms, introduces the crossover and mutation mechanism of the genetic algorithm into the particle swarm optimization algorithm, and proposes the GPSO-SVM algorithm, which generates new individuals through crossover and mutation operations after each iteration of the particle. e current individuals with low fitness are replaced to expand the search space where the particle swarm is constantly shrinking in the iteration so that the particles jump out of the local optimal value position currently searched and search in a larger space to find a better value [14].…”

Section: Gpso-svm Algorithmmentioning

confidence: 99%

A Classification Method of Network Ideological and Political Resources Using Improved SVM Algorithm

Wang

2021

Security and Communication Networks

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In order to improve the accuracy and efficiency of the classification of network ideological and political resources and promote the efficiency of ideological education, a research on the classification of network ideological and political resources based on the improved SVM algorithm is proposed. We analyze the characteristics and current situation of network ideological and political resources and conclude that the method elements are open and technical. The ontology elements are rich and shared, and the behavioral elements are autonomous and interactive. Three types of network ideological and political resources are proposed: the main resource, content resource, and means resource. The particle swarm algorithm is used to improve the SVM algorithm. In the process of constructing the SVM classifier, the fuzzy membership function is introduced, the classification problem of network ideological and political resources is converted into a secondary planning problem, and the accuracy of network ideological and political resources is finally realized. Simulation results show that the use of improved algorithms to classify network ideological and political resources can improve the accuracy and efficiency of network abnormal data classification.

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“…Optical remotely sensed imagery is unable to capture usable images during adverse weather conditions or at night. Furthermore, low resolution optical imagery exhibits the mixed pixel problem, and the similarities of spectral reflectance on landscape associated with optical images features are problematic (Muthukumarasamy et al, 2019). In comparison, Synthetic Aperture Radar (SAR) imagery captures imagery based on the geometry instead of the reflectance of the target features, overcoming some of these issues inherent to optical imagery (Muthukumarasamy et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, low resolution optical imagery exhibits the mixed pixel problem, and the similarities of spectral reflectance on landscape associated with optical images features are problematic (Muthukumarasamy et al, 2019). In comparison, Synthetic Aperture Radar (SAR) imagery captures imagery based on the geometry instead of the reflectance of the target features, overcoming some of these issues inherent to optical imagery (Muthukumarasamy et al, 2019). Furthermore, Krishna et al (2018) argue that hyperspectral remote sensing is a solution to the problem of mixed pixels and spectral similarity associated with optically derived imagery, due to the numerous bands of hyperspectral data that provide spectral information per pixel, allowing for the discrimination of feature classes, yielding improved LULC classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

The use of remote sensing and GIS for land use and land cover mapping in Eswatini: A Review

Simelane¹,

Hansen²,

Munghemezulu³

2022

SA J of Geomatics

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Remote sensing and GIS are often used to assess spatiotemporal variations for land use/land cover (LULC) monitoring and classification. While LULC monitoring and classification has been undertaken in Eswatini, little attention has been given to ascertaining covered thematic areas, methods of image classification, and approaches and techniques for improving classification accuracy. This paper summarises and synthesizes the progress made in the Kingdom of Eswatini regarding the application of remote sensing and GIS in LULC monitoring and classification. Eight thematic areas (water resources mapping; land degradation; forestry; wildfire detection; urban expansion; crop production; disease surveillance; general mapping) dominate evaluated LULC studies, employing three LULC classification methods (classic; manual; advanced). While some studies include strengths and weaknesses of LULC classification techniques applied, others do not. This review shows that only two advanced classifiers (random forest; object-based) were identified from the reviewed articles. In addition, reviewed studies applied only two approaches (use of multi temporal data; fine spatial resolution data) and three techniques (use of ancillary data; post-classification procedure; the use of multisource data) for improving classification accuracy. Furthermore, the review finds that limited LULC investigations have been covered in Eswatini with a specific focus on the Sustainable Development Goals (SDGs). As such, this review recommends 1) the inclusion of higher resolution imagery for mapping purposes, 2) the adaptation of strengths and weaknesses for any image classification technique employed in future publications, 3) the use of more varied approaches and techniques for improving classification accuracy and area estimates, 4) inclusion of standard errors or confidence intervals for error-adjusted area estimates as part of accuracy assessment reporting, 5) the application of advanced image classifiers, and 6) the application of Earth Observation (EO) Analysis Ready Data (ARD) in the production of information for the support of the SDGs.

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Incorporation of textural information with SAR and optical imagery for improved land cover mapping

Cited by 11 publications

References 41 publications

Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

Mapping Winter Wheat with Optical and SAR Images Based on Google Earth Engine in Henan Province, China

A Classification Method of Network Ideological and Political Resources Using Improved SVM Algorithm

The use of remote sensing and GIS for land use and land cover mapping in Eswatini: A Review

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