Forest classification using extracted PolSAR features from Compact Polarimetry data

Aghabalaei, Amir; Maghsoudi, Yasser; Ebadi, Hamid

doi:10.1016/j.asr.2016.02.007

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…Study areas, the species investigated, and the number of training and validation samples differed substantially between the studies. Nevertheless, our achieved accuracies were comparable to other studies classifying deciduous and coniferous forests using even fully polarimetric C-band SAR data [16,83,84]. Compared with other technologies such as airborne laser scanning (OA of 89-96% and κ = 0.61 − 0.92) [85][86][87] or imaging spectrometer data (OA of 83-99% and κ = 0.73 − 0.98) [88][89][90], our forest type classification performance (OA of 86% and κ = 0.73) was not as competitive.…”

Section: Classification Of Forest Types and Speciessupporting

confidence: 74%

Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland

2017

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Efficient methods to monitor forested areas help us to better understand their processes. To date, only a few studies have assessed the usability of multitemporal synthetic aperture radar (SAR) datasets in this context. Here we present an analysis of an unprecedented set of C-band observations of mixed temperate forests. We demonstrate the potential of using multitemporal C-band VV and VH polarisation data for monitoring phenology and classifying forests in northern Switzerland. Each SAR acquisition was first radiometrically terrain corrected using digital elevation model-based image simulations of the local illuminated area. The flattened backscatter values and the local area values were input to a temporal compositing process integrating backscatter values from ascending and descending tracks. The process used local resolution weighting of each input, producing composite backscatter values that strongly mitigated terrain-induced distortions. Several descriptors were calculated to show the seasonal variation of European beech (Fagus sylvatica), oak (Quercus robur, Quercus petraea) and Norway spruce (Picea abies) in C-band data. Using their distinct seasonal signatures, the timing of leaf emergence and leaf fall of the deciduous species were estimated and compared to available ground observations. Furthermore, classifications for the forest types 'deciduous' and 'coniferous' and the investigated species were implemented using random forest classifiers. The deciduous species backscatter was about 1 dB higher than spruce throughout the year in both polarisations. The forest types showed opposing seasonal backscatter behaviours. At VH, deciduous species showed higher backscatter in winter than in summer, whereas spruce showed higher backscatter in summer than in winter. In VV, this pattern was similar for spruce, while no distinct seasonal behaviour was apparent for the deciduous species. The time differences between the estimations and the ground observations of the phenological events were approximately within the error margin (±12 days) of the temporal resolution. The classification performances were promising, with higher accuracies achieved for the forest types (OA of 86% and κ = 0.73) than for individual species (OA of 72% and κ = 0.58). These results show that multitemporal C-band backscatter data have significant potential to supplement optical remote sensing data for ecological studies and mapping of mixed temperate forests.

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Section: Classification Of Forest Types and Speciessupporting

confidence: 74%

Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland

2017

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“…With advances in state-of-the-art artificial intelligence (AI) algorithms, several machine learning classifiers have been applied as a supervised method for FP and HCP SAR image classifications [35,36,38]. Feature extraction is one of the most important steps in supervised PolSAR image classification and has a direct impact on the classification accuracy.…”

Section: Number Of Features Numericmentioning

confidence: 99%

“…Given that maritime surveillance is one the main application of RCM data, the potential of simulated or real HCP SAR data has been well examined for sea ice classification and monitoring in several recent studies [35,62,65,66]. For example, [62] and [35] examined the discrimination With advances in state-of-the-art artificial intelligence (AI) algorithms, several machine learning classifiers have been applied as a supervised method for FP and HCP SAR image classifications [35,36,38]. Feature extraction is one of the most important steps in supervised PolSAR image classification and has a direct impact on the classification accuracy.…”

Section: Number Of Features Numericmentioning

confidence: 99%

“…RISAT-1 s HCP data became one of its most popular and successful products. Simulated and real HCP data have proven to be effective for a variety of applications, including sea ice classification [35], natural resource monitoring [36], disaster mitigation and monitoring [37], wetland monitoring [38], and crop mapping [39].…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, finding the most appropriate polarimetric parameters with sufficient information is of great importance for unsupervised PolSAR image classifications. These algorithms have been developed based on target decomposition techniques and polarimetric data distribution theories.With advances in state-of-the-art artificial intelligence (AI) algorithms, several machine learning classifiers have been applied as a supervised method for FP and HCP SAR image classifications[35,36,38]. Feature extraction is one of the most important steps in supervised PolSAR image classification and has a direct impact on the classification accuracy.…”

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confidence: 99%

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Hybrid Compact Polarimetric SAR for Environmental Monitoring with the RADARSAT Constellation Mission

Brisco¹,

Mahdianpari

Mohammadimanesh

2020

Remote Sensing

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Canada’s successful space-based earth-observation (EO) radar program has earned widespread and expanding user acceptance following the launch of RADARSAT-1 in 1995. RADARSAT-2, launched in 2007, while providing data continuity for its predecessor’s imaging capabilities, added new polarimetric modes. Canada’s follow-up program, the RADARSAT Constellation Mission (RCM), launched in 2019, while providing continuity for its two predecessors, includes an innovative suite of polarimetric modes. In an effort to make polarimetry accessible to a wide range of operational users, RCM uses a new method called hybrid compact polarization (HCP). There are two essential elements to this approach: (1) transmit only one polarization, circular; and (2) receive two orthogonal polarizations, for which RCM uses H and V. This configuration overcomes the conventional dual and full polarimetric system limitations, which are lacking enough polarimetric information and having a small swath width, respectively. Thus, HCP data can be considered as dual-pol data, while the resulting polarimetric classifications of features in an observed scene are of comparable accuracy as those derived from the traditional fully polarimetric (FP) approach. At the same time, RCM’s HCP methodology is applicable to all imaging modes, including wide swath and ScanSAR, thus overcoming critical limitations of traditional imaging radar polarimetry for operational use. The primary image data products from an HCP radar are different from those of a traditional polarimetric radar. Because the HCP modes transmit circularly polarized signals, the data processing to extract polarimetric information requires different approaches than those used for conventional linearly polarized polarimetric data. Operational users, as well as researchers and students, are most likely to achieve disappointing results if they work with traditional polarimetric processing tools. New tools are required. Existing tutorials, older seminar notes, and reference papers are not sufficient, and if left unrevised, could succeed in discouraging further use of RCM polarimetric data. This paper is designed to provide an initial response to that need. A systematic review of studies that used HCP SAR data for environmental monitoring is also provided. Based on this review, HCP SAR data have been employed in oil spill monitoring, target detection, sea ice monitoring, agriculture, wetland classification, and other land cover applications.

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Land Cover Classification Using Ensemble Techniques

Parikh

Patel

2019

Lecture Notes in Computer Science

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Forest classification using extracted PolSAR features from Compact Polarimetry data

Cited by 11 publications

References 25 publications

Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland

Using Multitemporal Sentinel-1 C-band Backscatter to Monitor Phenology and Classify Deciduous and Coniferous Forests in Northern Switzerland

Hybrid Compact Polarimetric SAR for Environmental Monitoring with the RADARSAT Constellation Mission

Land Cover Classification Using Ensemble Techniques

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