A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets

Gao, Han; Wang, Changcheng; Wang, Guanya; Fu, Haiqiang; Zhu, Jianjun

doi:10.1016/j.rse.2021.112628

Cited by 36 publications

(16 citation statements)

References 42 publications

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“…The diagonal elements of the covariance matrix (C 11 , C 22 ) represent the backscattering coefficients in VV, VH polarization channels [11], respectively, while C 12 , C 21 elements express the complex correlation of back-scattered information between VV, VH polarization channels [67]. Deriving the covariance matrix from the vectorized scattering matrix is essential for the accurate characterization of distributed targets, such as vegetation [99].…”

Section: Polarimetric Data Representationmentioning

confidence: 99%

“…The potential of Sentinel-1 PolSAR data has been moderately examined in several application contexts, such as land-cover mapping [42][43][44][45][46][47], crop monitoring [11,28,[48][49][50][51][52][53], crop yield estimation [54], crop damage detection [55], as well as in other specialized research areas, such as surface soil moisture estimation [56] and flood extent mapping [57]. Even though numerous studies have examined and demonstrated the contribution of PolSAR data collected from various SAR missions, such as Radarsat-2, in effective crop mapping [58][59][60], there is only a handful of relevant applications that utilize Sentinel-1 PolSAR data [32,42,[61][62][63][64][65][66][67]. This limited interest might be justified, considering that the pre-processing workflow of Sentinel-1 PolSAR data is a non-trivial and computationally expensive task.…”

Section: Introductionmentioning

confidence: 99%

“…These studies undertaken to explore the usefulness of Sentinel-1 PolSAR data exhibit high heterogeneity concerning the study area characteristics, as well as, the input datasets and classification algorithms that were employed. Some of these studies use only polarimetric input features to build their crop classification models, reporting satisfactory overall accuracies from up to 96% [32], while some of them incorporate polarimetric features as supplementary information in combination with Sentinel-1 backscattering data [64,65,67], reporting contradictory results about the added value of PolSAR data. In most cases, H, A, α parameters derived from Cloude-Pottier polarimetric decomposition were used, as well as the elements of the covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification

et al. 2022

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Crop classification is an important remote sensing task with many applications, e.g., food security monitoring, ecosystem service mapping, climate change impact assessment, etc. This work focuses on mapping 10 crop types at the field level in an agricultural region located in the Spanish province of Navarre. For this, multi-temporal Synthetic Aperture Radar Polarimetric (PolSAR) Sentinel-1 imagery and multi-spectral Sentinel-2 data were jointly used. We applied the Cloude–Pottier polarimetric decomposition on PolSAR data to compute 23 polarimetric indicators and extracted vegetation indices from Sentinel-2 time-series to generate a big feature space of 818 features. In order to assess the relevance of the different features for the crop mapping task, we run a number of scenarios using a Support Vector Machines (SVM) classifier. The model that was trained using only the polarimetric data demonstrates a very promising performance, achieving an overall accuracy over 82%. A genetic algorithm was also implemented as a feature selection method for deriving an optimal feature subset. To showcase the positive effect of using polarimetric data over areas suffering from cloud coverage, we contaminated the original Sentinel-2 time-series with simulated cloud masks. By incorporating the genetic algorithm, we derived a high informative feature subset of 120 optical and polarimetric features, as the corresponding classification model increased the overall accuracy by 5% compared to the model trained only with Sentinel-2 features. The feature importance analysis indicated that apart from the Sentinel-2 spectral bands and vegetation indices, several polarimetric parameters, such as Shannon entropy, second eigenvalue and normalised Shannon entropy are of high value in identifying crops. In summary, the findings of our study highlight the significant contribution of Sentinel-1 PolSAR data in crop classification in areas with frequent cloud coverage and the effectiveness of the genetic algorithm in discovering the most informative features.

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Section: Polarimetric Data Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification

et al. 2022

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“…Synthetic aperture radar (SAR) is an active microwave sensor, which cannot be affected by cloudy and foggy weather [16]. Moreover, SAR is sensitive to the dielectric properties and structure of plants and is very suitable for monitoring and classifying crops [17,18]. In addition, polarimetric SAR (PolSAR), which can provide rich information, can extract a large number of polarimetric features.…”

Section: Introductionmentioning

confidence: 99%

Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data

Tian

et al. 2022

Remote Sensing

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Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification.

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“…Support vector machine (SVM) can be an appropriate classifier when limited training samples are available 20 . The pairwise proximity function SVM is proposed in 21 for time series analysis of dual polarization SAR for crop classification.…”

Section: Introductionmentioning

confidence: 99%

Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence

Imani

2022

Sci Rep

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Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but they need a high volume of labeled samples, which are not usually available in practice, or they cause a high computational burden for implementation. In this work, instead of spending cost for network training, the inherent nature of PolSAR image is used for generation of convolutional kernels for extraction of deep and robust features. Moreover, extraction of diverse scattering characteristics contained in the coherency matrix of PolSAR and fusion of their output classification results with a high confidence have high impact in providing a reliable classification map. The introduced method called discriminative features based high confidence classification (DFC) utilizes several approaches to deal with difficulties of PolSAR image classification. It uses a multi-view analysis to generate diverse classification maps with different information. It extracts deep polarimetric-spatial features, consistent and robust with respect to the original PolSAR data, by applying several pre-determined convolutional filters selected from the important regions of image. Convolutional kernels are fixed without requirement to be learned. The important regions are determined with selecting the key points of image. In addition, a two-step discriminant analysis method is proposed to reduce dimensionality and result in a feature space with minimum overlapping and maximum class separability. Eventually, a high confidence decision fusion is implemented to find the final classification map. Impact of multi-view analysis, selection of important regions as fixed convolutional kernels, two-step discriminant analysis and high confidence decision fusion are individually assessed on three real PolSAR images in different sizes of training sets. For example, the proposed method achieves 96.40% and 98.72% overall classification accuracy by using 10 and 100 training samples per class, respectively in L-band Flevoland image acquired by AIRSAR. Generally, the experiments show high efficiency of DFC compared to several state-of-the-art methods especially for small sample size situations.

show abstract

A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets

Cited by 36 publications

References 42 publications

Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification

Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification

Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data

Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence

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