Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods

Imangholiloo, Mohammad; Rasinmäki, Jussi; Rauste, Yrjö; Holopainen, Markus

doi:10.1080/07038992.2019.1635877

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Machine learning algorithms have a crucial impact on the results of classification [60]. The classical pixel-based machine learning algorithms, RF, KNN, etc., have a wide range of applications in LCC [61,62].…”

Section: Comparative Analysis Of Different Classification Methodsmentioning

confidence: 99%

Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index

Huang,

Meng,

Hou

et al. 2023

Remote Sensing

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Accurate land cover classification (LCC) is essential for studying global change. Synthetic aperture radar (SAR) has been used for LCC due to its advantage of weather independence. In particular, the dual-polarization (dual-pol) SAR data have a wider coverage and are easier to obtain, which provides an unprecedented opportunity for LCC. However, the dual-pol SAR data have a weak discrimination ability due to limited polarization information. Moreover, the complex imaging mechanism leads to the speckle noise of SAR images, which also decreases the accuracy of SAR LCC. To address the above issues, an improved dual-pol radar vegetation index based on multiple components (DpRVIm) and a new LCC method are proposed for dual-pol SAR data. Firstly, in the DpRVIm, the scattering information of polarization and terrain factors were considered to improve the separability of ground objects for dual-pol data. Then, the Jeffries-Matusita (J-M) distance and one-dimensional convolutional neural network (1DCNN) algorithm were used to analyze the effect of difference dual-pol radar vegetation indexes on LCC. Finally, in order to reduce the influence of the speckle noise, a two-stage LCC method, the 1DCNN-MRF, based on the 1DCNN and Markov random field (MRF) was designed considering the spatial information of ground objects. In this study, the HH-HV model data of the Gaofen-3 satellite in the Dongting Lake area were used, and the results showed that: (1) Through the combination of the backscatter coefficient and dual-pol radar vegetation indexes based on the polarization decomposition technique, the accuracy of LCC can be improved compared with the single backscatter coefficient. (2) The DpRVIm was more conducive to improving the accuracy of LCC than the classic dual-pol radar vegetation index (DpRVI) and radar vegetation index (RVI), especially for farmland and forest. (3) Compared with the classic machine learning methods K-nearest neighbor (KNN), random forest (RF), and the 1DCNN, the designed 1DCNN-MRF achieved the highest accuracy, with an overall accuracy (OA) score of 81.76% and a Kappa coefficient (Kappa) score of 0.74. This study indicated the application potential of the polarization decomposition technique and DEM in enhancing the separability of different land cover types in SAR LCC. Furthermore, it demonstrated that the combination of deep learning networks and MRF is suitable to suppress the influence of speckle noise.

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Section: Comparative Analysis Of Different Classification Methodsmentioning

confidence: 99%

Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index

Huang,

Meng,

Hou

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

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“…The second algorithm used was Random Forest (RF). RF is a derivative of Decision Tree which provides an improvement over DT to overcome the weaknesses of a single DT (Pal 2005;He et al 2017;Vlachopoulos et al 2020). The prediction model of the RF classifier only requires two parameters to be identified: the number of classification trees desired, known as "ntree," and the number of prediction variables, known as "mtry," used in each node to make the tree grow (Rodriguez-Galiano et al 2012).…”

Section: Classification Methodsmentioning

confidence: 99%

Fusing Landsat and SAR Data for Mapping Tropical Deforestation through Machine Learning Classification and the PVts-β Non-Seasonal Detection Approach

Tarazona

Zabala

Pons

et al. 2021

Canadian Journal of Remote Sensing

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This article focuses on mapping tropical deforestation using time series and machine learning algorithms. Before detecting changes in the time series, we reduced seasonality using Photosynthetic Vegetation (PV) index fractions obtained from Landsat images. Single and multi-temporal filters were used to reduce speckle noise from Synthetic Aperture Radar (SAR) images (i.e., ALOS PALSAR and Sentinel-1B) before fusing them with optical images through Principal Component Analysis (PCA). We detected only one change in the two PV series using a non-seasonal detection approach, as well as in the fused images through five machine learning algorithms that were calibrated with Cross-Validation (CV) and Monte Carlo Cross-Validation (MCCV). In total, four categories were obtained: forest, cropland, bare soil, and water. We then compared the change map obtained with time series and that obtained with the classification algorithms with the best calibration performance, revealing an overall accuracy of 92.91% and 91.82%, respectively. For statistical comparisons, we used deforestation reference data. Finally, we conclude with some discussions and reflections on the advantages and disadvantages of the detections made with time series and machine learning algorithms, as well as the contribution of SAR images to the classifications, among other aspects. RÉSUMÉ Cet article porte sur la cartographie de la d eforestation tropicale a l'aide de s eries chronologiques et d'algorithmes d'apprentissage automatique. Avant de d etecter les changements dans les s eries temporelles, nous avons r eduit la saisonnalit e en utilisant les fractions de l'indice de v eg etation photosynth etique (PV) obtenues a partir des images Landsat. Des filtres mono et multitemporels ont et e appliqu es pour r eduire le bruit de chatoiement des images RSO (Radar a synth ese d'ouverture) (c'est-a-dire ALOS PALSAR et Sentinel-1B) avant de les fusionner avec les images optiques par le biais de l'analyze en composantes principales (ACP). Nous avons d etect e un seul changement a la fois dans les s eries de PV en utilisant une approche de d etection non saisonni ere, ainsi que dans les images fusionn ees, grâce a cinq algorithmes d'apprentissage automatique qui ont et e calibr es par validation crois ee (CV) et validation crois ee de Monte-Carlo (MCCV). Au total, quatre cat egories ont et e obtenues: forêt, terres cultiv ees, sol nu et eau. Ensuite, nous avons compar e la carte des changements obtenue avec les s eries temporelles et celle obtenue avec les algorithmes de classification les plus performants en mati ere de calibration, r ev elant une pr ecision globale ARTICLE HISTORY

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Object-Oriented and Deep-Learning-Based High-Resolution Mapping from Large Remote Sensing Imagery

Zhang

et al. 2021

Canadian Journal of Remote Sensing

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Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods

Cited by 4 publications

References 13 publications

Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index

Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index

Fusing Landsat and SAR Data for Mapping Tropical Deforestation through Machine Learning Classification and the PVts-β Non-Seasonal Detection Approach

Object-Oriented and Deep-Learning-Based High-Resolution Mapping from Large Remote Sensing Imagery

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