Crop Classification Based on Differential Characteristics of &lt;inline-formula&gt; 
                     &lt;tex-math notation="LaTeX"&gt;$H/\alpha$ &lt;/tex-math&gt;
                  &lt;/inline-formula&gt; Scattering Parameters for Multitemporal Quad- and Dual-Polarization SAR Images

Guo, Jun; Wei, Pengliang; Liu, Jian; Jin, Biao; Su, Baofeng; Zhou, Zhenxing

doi:10.1109/tgrs.2018.2832054

Cited by 49 publications

(23 citation statements)

References 44 publications

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“…To date, the majority of research on cropland classification was done using multiparametric SAR data [10]. This includes mostly polarimetric and multitemporal SAR data [8,[11][12][13][14][15][16][17][18][19][20][21][22], as well as multi-frequency SAR and fusion of satellite optical and SAR data [7,23]. In addition, the potential of interferometric SAR approaches was evaluated along with SAR backscatter data in crop monitoring [6,24,25].…”

Section: Sar Data In Crop Classificationmentioning

confidence: 99%

Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment

2019

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Methods based on Sentinel-1 data were developed to monitor crops and fields to facilitate the distribution of subsidies. The objectives were to (1) develop a methodology to predict individual crop species or or management regimes; (2) investigate the earliest time point in the growing season when the species predictions are satisfactory; and (3) to present a method to assess the uncertainty of the predictions at an individual field level. Seventeen Sentinel-1 synthetic aperture radar (SAR) scenes (VV and VH polarizations) acquired in interferometric wide swath mode from 14 May through to 30 August 2017 in the same geometry, and selected based on the weather conditions, were used in the study. The improved k nearest neighbour estimation, ik-NN, with a genetic algorithm feature optimization was tailored for classification with optional Sentinel-1 data sets, species groupings, and thresholds for the minimum parcel area. The number of species groups varied from 7 to as large as 41. Multinomial logistic regression was tested as an optional method. The Overall Accuracies (OA) varied depending on the number of species included in the classification, and whether all or not field parcels were included. OA with nine species groups was 72% when all parcels were included, 81% when the parcels area threshold (for incorporating parcels into classification) was 0.5 ha, and around 90% when the threshold was 4 ha. The OA gradually increased when adding extra Sentinel-1 scenes up until the early August, and the initial scenes were acquired in early June or mid-May. After that, only minor improvements in the crop recognition accuracy were noted. The ik-NN method gave greater overall accuracies than the logistic regression analysis with all data combinations tested. The width of the 95% confidence intervals with ik-NN for the estimate of the probability of the species with the largest probability on an individual parcel varied depending on the species, the area threshold of the parcel and the number of the Sentinel-1 scenes used. The results ranged between 0.06-0.08 units (6-8% points) for the most common species when the Sentinel-1 scenes were between 1 June and 12 August. The results were well-received by the authorities and encourage further research to continue the study towards an operational method in which the space-borne SAR data are a part of the information chain.

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Section: Sar Data In Crop Classificationmentioning

confidence: 99%

Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment

2019

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“…Nowadays, several representative systems are available for civilian applications, including C-band Sentinel-1 systems [20,21], RADARSAT-2 and Radarsat Constellation Mission (RCM) [22,23], L-band Advanced Land Observing Satellite (ALOS) ALOS-PALSAR/PALSAR-2 [24,25], X-band Tandem-X [26], and X-band Constellation of Small Satellites for Mediterranean basin Observation (COMSMO) COMSMO -SkyMed constellation [27]. Based on these operational systems, large amounts of multi-temporal PolSAR data can be collected and adopted for use in crop classification and other applications [28][29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…Zhou et al used extracted SAR features in multiple deep convolutional networks (DCNs) as the input features of LSTM to improve the classification accuracy [30]. On the other hand, time-series or multi-temporal information is extracted manually from multi-temporal and multi-source data [31][32][33][34][35][36]. For example, Zhong et al designed a one-dimensional convolution network to extract time-series features and discriminate different ground objects [31].…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al combined Normalized Difference Vegetation Index (NDVI) of optical images with SAR data to raise the accuracy of classifying paddy-rice in mountainous areas [32]. Guo et al defined a new parameter based on differential characteristics of Cloude scattering parameters to improve the crop classification accuracy [33]. It can be seen that there is a rapid development in the analysis and application of multi-temporal optical and SAR data.…”

Section: Introductionmentioning

confidence: 99%

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Feature Dimension Reduction Using Stacked Sparse Auto-Encoders for Crop Classification with Multi-Temporal, Quad-Pol SAR Data

Guo

Ning

et al. 2020

Remote Sensing

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Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop classification accuracies since the same crop shows different external phenomena as it grows up. In practice, the utilization of multi-temporal data encounters a serious problem known as a “dimension disaster”. Aiming to solve this problem and raise the classification accuracy, this study developed a feature dimension reduction method using stacked sparse auto-encoders (S-SAEs) for crop classification. First, various incoherent scattering decomposition algorithms were employed to extract a variety of detailed and quantitative parameters from multi-temporal PolSAR data. Second, based on analyzing the configuration and main parameters for constructing an S-SAE, a three-hidden-layer S-SAE network was built to reduce the dimensionality and extract effective features to manage the “dimension disaster” caused by excessive scattering parameters, especially for multi-temporal, quad-pol SAR images. Third, a convolutional neural network (CNN) was constructed and employed to further enhance the crop classification performance. Finally, the performances of the proposed strategy were assessed with the simulated multi-temporal Sentinel-1 data for two experimental sites established by the European Space Agency (ESA). The experimental results showed that the overall accuracy with the proposed method was raised by at least 17% compared with the long short-term memory (LSTM) method in the case of a 1% training ratio. Meanwhile, for a CNN classifier, the overall accuracy was almost 4% higher than those of the principle component analysis (PCA) and locally linear embedded (LLE) methods. The comparison studies clearly demonstrated the advantage of the proposed multi-temporal crop classification methodology in terms of classification accuracy, even with small training ratios.

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“…The polarimetric information of a target can be represented in form of a 2x2 covariance matrix ([C2]), which would be helpful for polarimetric analysis. A very few studies indicated the potential of using polarimetric information from [C2] for several applications [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-1 SLC Preprocessing Workflow for Polarimetric Applications: A Generic Practice for Generating Dual-pol Covariance Matrix Elements in SNAP S-1 Toolbox

Mandal¹,

Vaka²,

Bhogapurapu³

et al. 2019

Preprint

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Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matrix from Sentinel-1 single look complex (SLC) data, several preprocessing steps are required. The ESA SNAP S-1 toolbox can be used to preprocess the data to generate a [C2] matrix. The polarimetric analysis in respective application fields often starts with the covariance matrix. However, due to limited availability of Sentinel-1 SLC data preprocessing workflow standards for polarimetric applications in contemporary research methods, downstream applications unable to comply with these workflows directly. In this paper, we propose a couple of generic practices to preprocess Sentinel-1 SLC data in SNAP S-1 toolbox, which would be beneficial for the radar remote sensing user community. Single and multi-date data preprocessing workflow for Sentinel-1  A generic workflow to obtain dual-pol covariance matrix elements from SLC products  Accurate sub-pixel level coregistration of multi-date data

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Crop Classification Based on Differential Characteristics of <inline-formula> <tex-math notation="LaTeX">$H/\alpha$ </tex-math> </inline-formula> Scattering Parameters for Multitemporal Quad- and Dual-Polarization SAR Images

Cited by 49 publications

References 44 publications

Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment

Cropland Classification Using Sentinel-1 Time Series: Methodological Performance and Prediction Uncertainty Assessment

Feature Dimension Reduction Using Stacked Sparse Auto-Encoders for Crop Classification with Multi-Temporal, Quad-Pol SAR Data

Sentinel-1 SLC Preprocessing Workflow for Polarimetric Applications: A Generic Practice for Generating Dual-pol Covariance Matrix Elements in SNAP S-1 Toolbox

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