Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data

Varghese, Alappat Ouseph; Suryavanshi, Arun; Joshi, Asokh Kumar

doi:10.1080/01431161.2015.1136448

Cited by 45 publications

(15 citation statements)

References 29 publications

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“…The study region is mostly dominated by a series of hill ranges with forest coverage up to 75%. This is in line with the other previous studies which suggested that the four-component decomposition scheme comprising the scattering contributions from surface (single-bounce), double-bounce, volume, and helix performed better than the three-component decomposition scheme [23,59]. Notably, Varghese et al [21] adopted six decomposition methods (i.e.…”

Section: Discussionsupporting

confidence: 89%

“…SVM classifier discriminates the classes by fitting an optimal separating hyperplane to the training samples in a multi-dimensional feature space [58]. It uses kernel function to make nonlinear decision boundaries into linear ones in a high-dimensional space [58,59]. In this study, the SVM classification was performed using Pol-SARpro 5.0 software, wherein two decomposition images (FDD and YD) were used as an input data to classifier.…”

Section: Support Vector Machine (Svm) Classifiermentioning

confidence: 99%

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Polarimetric decomposition methods for LULC mapping using ALOS L-band PolSAR data in Western parts of Mizoram, Northeast India

Parida

Mandal

2020

SN Appl. Sci.

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The rapid advancement of remote sensing and availability of polarimetric SAR (PolSAR) data have facilitated to monitor the land use land cover (LULC) dynamics. In the recent past, polarimetric decomposition theorems are applied widely to perform LULC classification with the help of machine learning techniques. In this study, we utilized ALOS PALSAR-1 L-band quad polarimetric data for performing polarimetric decomposition, textural information extraction, and to generate LULC maps over the western part of Mizoram state, northeast India. The study area comprises three districts, namely Mamit, Lunglei, and Lawngtlai. We adopted two representative full-polarimetric decomposition models: classical model-based Freeman-Durden and Yamaguchi decomposition. These methods decompose the coherency matrix of PolSAR images into surface, double-bounce, and volume scattering. Textural measures, such as variance, contrast, entropy, homogeneity, dissimilarity, and uniformity are also retrieved using grey-level co-occurrence matrix (GLCM) for LULC classification. For LULC classification, we employed a support vector machine classifier and calculated the area statistics of LULC. The outcomes were checked with the help of confusion matrix derived for six classes, such as built-up, deciduous forest, evergreen forest, scrubland, bareland, and waterbody. Each LULC class is separated using the scattering properties of PolSAR images. Results exhibited that Yamaguchi four-component decomposition (overall accuracy 90% and kappa coefficient 0.88) gives relatively better LULC classification results than the Freeman-Durden three-component decomposition (overall accuracy 87% and kappa coefficient 0.84). Use of textural images of GLCM has supported the classification accuracy at par with the Yamaguchi model. Integration of polarimetric information offers a new dimension in LULC classification and produces high accuracy maps. This approach overcomes the limitations of optical data in cloud covering areas, and furthermore, it provides better classification accuracy.

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Section: Discussionsupporting

confidence: 89%

Section: Support Vector Machine (Svm) Classifiermentioning

confidence: 99%

Polarimetric decomposition methods for LULC mapping using ALOS L-band PolSAR data in Western parts of Mizoram, Northeast India

Parida

Mandal

2020

SN Appl. Sci.

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“…In this study, the YH polarimetric decomposition classified by the RF algorithm showed the best results in terms of OA and Kappa index for most of the thematic classes, including forest degradation class, in both years. In this context, Varghese et al [32] analyzed different polarimetric decompositions to classify forest canopy density, based on the full polarimetric RADARSAT-2 data classified by the SVM algorithm. YH decomposition also showed the best results, followed by VZ, FD, and CP decompositions.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, synthetic aperture radar (SAR) sensors are almost independent of atmospheric conditions and are sensitive to variations in forest biomass and structure [29,30]. SAR data allow a proper assessment of different LULC classes and forest degradation, especially by fire, even under cloudy conditions, or even under smoke conditions during active fires, as noted in some Amazonia rainforest sites [31,32]. Radar sensors with longer wavelengths, such as the one onboard the Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) satellite, present higher penetration of transmitted microwave signals into the forest canopy when compared with radar sensors with shorter wavelengths, such as the X-band, TerraSAR/TanDEM-X and Cosmo-SkyMed satellites, or the C-band, RADARSAT-2, and Sentinel-1A/1B satellites [33].…”

Section: Introductionmentioning

confidence: 99%

Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data

et al. 2020

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We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude–Pottier, van Zyl, Freeman–Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individual backscattering indexes and polarimetric decompositions were also considered in both RF and SVM classifiers. Yamaguchi decomposition performed by RF presented the best results, with an overall accuracy (OA) of 76.9% and 83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, respectively. The optimum subset classified by RF showed an OA of 75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 2016, respectively. RF exhibited superior performance in relation to SVM in both years. Polarimetric attributes exhibited an adequate capability to discriminate forest degradation and classes of different ecological succession from the ones with less vegetation cover.

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“…For the radar images, studies have shown that the L-band wavelength has a stronger relationship with stems, whereas the C-band wavelength interacts more with the canopy [76,77]. The RFR and SVR model were developed by using only γ 0 HH , γ 0 HV , γ 0 VV , and γ 0 VH to estimate the volume, and the RFR resulting had moderate accuracy.…”

Section: Radar Variablesmentioning

confidence: 99%

Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan

et al. 2020

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The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems (UASs) highlight the importance of modern technologies in the realm of forest observation. Each technology has different advantages, and this work seeks to incorporate multiple satellite, TLS- and UAS-based remote sensing data sets to improve the ability to estimate forest structure parameters. In this paper, two regression analysis approaches are considered for the estimation: random forest regression (RFR) and support vector regression (SVR). To collect the dependent variable, in situ measurements of individual tree parameters (tree height and diameter at breast height (DBH)) were taken in a Japanese cypress forest using the nondestructive TLS method, which scans the forest to obtain dense and accurate point clouds under the tree canopy. Based on the TLS data, the stem volume was then computed and treated as ground truth information. Topographic and UAS information was then used to calculate various remotely sensed explanatory variables, such as canopy size, canopy cover, and tree height. Canopy cover and canopy shapes were computed via the orthoimages derived from the UAS and watershed segmentation method, respectively. Tree height was computed by combining the digital surface model (DSM) from the UAS and the digital terrain model (DTM) from the TLS data. Topographic variables were computed from the DTM. The backscattering intensity in the satellite imagery was obtained based on L-band (Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)) and C-band (Sentinel-1) synthetic aperture radar (SAR). All satellite (10–25 m resolution), TLS (3.4 mm resolution) and UAS (2.3–4.6 cm resolution) data were then combined, and RFR and SVR were trained; the resulting predictive powers were then compared. The RFR method yielded fitting R2 up to 0.665 and RMSE up to 66.87 m3/ha (rRMSE = 11.95%) depending on the input variables (best result with canopy height, canopy size, canopy cover, and Sentinel-1 data), and the SVR method showed fitting R2 up to 0.519 and RMSE up to 80.12 m3/ha (rRMSE = 12.67%). The RFR outperformed the SVR method, which could delineate the relationship between the variables for better model accuracy. This work has demonstrated that incorporating various remote sensing data to satellite data, especially adding finer resolution data, can provide good estimates of forest parameters at a plot level (10 by 10 m), potentially allowing advancements in precision forestry.

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Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data

Cited by 45 publications

References 29 publications

Polarimetric decomposition methods for LULC mapping using ALOS L-band PolSAR data in Western parts of Mizoram, Northeast India

Polarimetric decomposition methods for LULC mapping using ALOS L-band PolSAR data in Western parts of Mizoram, Northeast India

Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data

Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan

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