Search

With the rapid advancements in SAR systems aiming for operational capabilities, crop characterization using compact-polarimetric synthetic aperture radar (CP-SAR) data has gained considerable attention. This study thoroughly assesses the potential usefulness of C-band SAR data in CP mode using the RADARSAT Constellation Mission (RCM) for crop monitoring. The research unfolds across two separate phases: 1) Extensive crop scattering characterization and 2) Crop classification. In the first part, we introduce three descriptors: compact-polarimetric SAR signature (CPS), differential CPS (DCPS), and the geodesic distance (GD) between signatures, to characterize the scattering pattern of four crop types: soybean, hay, corn, and cereal. We, then, derive the $\mu$ parameter and employ it in the $\mu -\chi$ decomposition method. Time-series investigation of the proposed descriptors and the three power components: $P_{s}$, $P_{d}$, and $P_{v}$ provide valuable insights into the scattering responses exhibited by crops, facilitating a robust assessment and tracking of their growing cycle, thus, enabling the potential for improving crop discrimination. In the second part, we employ the $\mu -\chi$ and $m-\chi$ decompositions and wave descriptors to extract a stack of CP features for crop mapping. Combining diverse feature types and leveraging single- and multi-date RCM images, classification experiments yield an optimal classification map with an overall accuracy of 89.71%, particularly when utilizing features extracted from multi-date datasets. This study illustrates a substantial effort in crop classification, underscoring the potential of the RCM CP-SAR mission. Furthermore, our findings emphasize the potential of CP-SAR data from the RCM mission in contributing to precision agriculture and sustainable crop management practices.

The RADARSAT Constellation Mission (RCM) Compact Polarimetry (CP) data has become a key asset in crop mapping and monitoring for diverse agricultural landscapes. This study utilizes the unique capabilities of the RCM CP data for crop mapping. It performs a detailed comparison between single-date and multi-date classification to underscore the prowess of multi-temporal CP data in crop mapping. The novelty of our approach is in the thorough investigation of real CP data, a significant advancement from previous studies that mainly relied on simulated CP data. The CP data used in this study were acquired on July 1, July 30, and August 27, 2021, over southern Quebec, Canada, including soy, corn, hay, and cereal classes. Various features were extracted from the CP data, and the Random Forest classifier was utilized for crop mapping. The experimental results demonstrated the superiority of multi-temporal CP data for crop classification. The Overall Accuracy (OA) for single-date classifications on July 1, July 30, and August 27 were 61.10%, 75.00%, and 86.45%, respectively. In contrast, the multi-date analysis showed a marked increase in OA (91.20%). This substantial improvement underscores the significant benefit of incorporating multi-date CP data, which delivers a robust and precise framework for crop mapping.

Oil spills pose a significant threat to the maritime ecosystem. Identifying an oil spill is vital to assess its spread and drift to nearby coastal areas. Synthetic aperture radar (SAR) sensors are viable for mapping and monitoring marine oil spills. This study proposes a new technique that utilizes the dual-polarimetric Sentinel-1 SAR data. The method is based on projecting the $2 \times 2$ covariance matrix onto distinct random realizations of the normalized scattering configuration. We then obtain the dual-polarimetric spectrum of the scattering-type parameter, $\theta _{\text{DP}}$. The $\theta _{\text{DP}}$ spectrum is then used in the unsupervised K-means clustering technique to segment oil spills from the rest. The cluster findings are then compared to the accuracies obtained using the standard scattering-type parameters from the eigen-decomposition approach (VV, VH) intensities and Otsu thresholding of [$H$ + $\alpha$ + A] parameter. We demonstrate the proposed approach by clustering marine oil-spill extent over parts of India, Kuwait, the UAE, and the Mediterranean Sea obtained by Sentinel-1 SAR images. We observed that the clustering accuracy of the proposed technique outperforms the ones obtained from the channel (i.e., VV and VH) intensities, Otsu thresholding of [$H$ + $\alpha$ + A] parameter, and the eigen-decomposition-based method. The proposed approach improves the overall accuracy by $\approx\! 8\%$ and $\approx\!20\%$, respectively, over different study areas.

Accurate mapping of forest above ground biomass (AGB) is essential for understanding changes in the rate of ecosystem processes such as biomass productivity, litter productivity, actual litter decomposition, and potential litter decomposition during secondary succession. They also play a vital role in evaluating forest carbon pools. This study presents a Matérn kernel-based Gaussian process regression (GPR) approach for biomass estimation integrating Synthetic Aperture Radar (SAR) backscatter intensities with LiDAR measurements. We use the backscatter intensities from five scenes of L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) simulated NISAR data collected during the 2019 AM–PM NASA airborne campaign. The biomass map derived from LVIS (Land, Vegetation, and Ice Sensor) Lidar point clouds over the Lenoir landing site, Alabama, was used with these simulated NISAR data. We have utilized the GPR model to estimate the AGB of the entire forest alongside the major forest classes in the study area, namely deciduous, evergreen, and woody wetlands. To examine the dependency of the model on acquisition conditions, we calibrated and validated our proposed model utilizing scene combinations. The experiment shows that multiple-scene retrieval delivered improved AGB estimates compared to single-scene retrieval. We evaluated the efficacy of the GPR model for three AGB ranges, i.e., (i) 8to100Mgha−1, (ii) 8to230Mgha−1, and (iii) 8to470Mgha−1 at 20m, 30m, 50m and 100m spatial resolution, respectively. The results indicate that the RMSE incurred by the GPR model for all these AGB ranges seemed to reduce as we increased spatial extent or reduced spatial resolution from 20m to 100m. Further, we demonstrate the performance of the optimized GPR model to retrieve AGB within the biomass range of 8Mgha−1 to 100Mgha−1 for all the above-mentioned forest types. Finally, this research highlights the advantages of the Matérn kernel-based GPR model over other statistical regression models towards improved AGB mapping.

Abstract In this paper, an advanced integrator is proposed for the estimation of stator flux in a direct torque control (DTC) drive. A two‐level lookup table‐based direct torque‐controlled interior permanent magnet synchronous motor (IPMSM) drive is implemented. DTC has the inherent disadvantage of DC drift and saturation in stator flux estimation due to a pure integrator. In the proposed work, a different approach to estimate stator flux is reported overcoming the constraints of a pure integrator in DTC. An advanced integrator with an adaptive controller is implemented to determine the stator flux and enrich the performance of the drive. A comparison of transient and steady‐state analysis is carried out when IPMSM is fed by a low‐pass filter‐based DTC and proposed stator flux estimator‐based DTC. To make the system mechanically robust, stator current‐based model reference adaptive control is executed to estimate the motor speed. A mathematical derivation for the stability study of the adaptation mechanism is carried out using Popov's hyperstability criteria. The proposed algorithm is implemented in the MATLAB/Simulink environment and the soundness of the proposed work is presented by simulation results. Moreover, the experimental analysis using dSPACE1104 justifies the simulation results of the proposed system.

Target decomposition methods from polarimetric Synthetic Aperture Radar (PolSAR) data provides target scattering information. In this regard, several conventional model-based methods use scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume ad hoc scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the occurrence of negative power components. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the nonconventional 3-D Barakat degree of polarization to obtain the polarization state of scattered electromagnetic wave. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, nonnegative, and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters’ variations from one target to another according to their physical and geometrical properties. In this study, we utilized L-, C-, and X-band full-polarimetric SAR data. We used these three datasets to show the effectiveness of decomposition powers and the natural interpretability of clustering results. The code is available at: https://github.com/Subho07/MF4CF

In this article, we address class incremental learning (IL) in remote sensing image analysis. Since remote sensing images are acquired continuously over time by Earth's observation sensors, the land-cover/land-use classes on the ground are likely to be found in a gradational manner. This process restricts the deployment of stand-alone classification approaches, which are trained for all the classes together in one iteration. Therefore, for every new set of categories discovered, the entire network consisting of old and new classes requires retraining. This procedure is often impractical, considering vast volumes of data, limited resources, and the complexity of learning models. In this respect, we propose a convolutional-neural-network-based framework (called CILEA-NET, curriculum-based incremental learning framework for remote sensing image classification) to efficiently resolve the difficulties associated with incremental learning paradigm. The framework includes new classes in the already trained model to avoid catastrophic forgetting for the old while ensuring improved generalization for the newly added classes. To manage the IL's stability-plasticity dilemma, we introduce a novel curriculum learning-based approach where the order of the new classes is devised based on their similarity to the already trained classes. We then perform the training in that given order. We notice that the curriculum learning setup distinctly enhances the training time for the new classes. Experimental results on several optical datasets: PatternNet and NWPU-RESISC45, and a hyperspectral dataset: Indian Pines, validate the robustness of our technique.

Conventional remote sensing data analysistechniques have a significant bottleneck of operating on a selectively chosen small-scale dataset. Availability of an enormous volume of data demands handling large-scale, diverse data, which have been made possible with neural network-based architectures. This article exploits the contextual information capturing ability of deep neural networks, particularly investigating multispectral band properties from Sentinel-2 image patches. Besides, an increase in the spatial resolution often leads to nonlinear mixing of land-cover types within a target resolution cell. We recognize this fact and group the bands according to their spatial resolutions, and propose a classification and retrieval framework. We design a representation learning framework for classifying the multispectral data by first utilizing all the bands and then using the grouped bands according to their spatial resolutions. We also propose a novel triplet-loss function for multilabeled images and use it to design an interband group retrieval framework. We demonstrate its effectiveness over the conventional triplet-loss function. Finally, we present a comprehensive discussion of the obtained results. We thoroughly analyze the performance of the band groups on various land-cover and land-use areas from agro-forestry regions, water bodies, and human-made structures. Experimental results for the classification and retrieval framework on the benchmarked BigEarthNet dataset exhibit marked improvements over existing studies.

A general framework for change detection is proposed to analyze multimodal remotely sensed data utilizing the Kronecker product between two data representations (vectors or matrices). The proposed method is sensor independent and provides comparable results to techniques that exist for specific sensors. The proposed fusion technique is a pixel-level approach that incorporates inputs from different modalities, rendering enriched multimodal data representation. Thus, the proposed hybridization procedure helps to assimilate multisensor information in a meaningful manner. A novel change index ($\zeta$) is defined for the general multimodal case. This index is then used to quantify the change in bitemporal remotely sensed data. This article explores the usability, consistency, and robustness of the proposed multimodal fusion framework, including the change index, with proper validation on two multimodal cases: 1) the dual-frequency ($C$- and $L$-band) fully polarimetric Danish EMISAR data and 2) the dual-polarimetric synthetic aperture radar and Sentinel-2 multispectral data. Detailed analysis and validation using extensive ground-truth data are presented to establish the proposed framework.

This study presents a simplified control algorithm for constant active power delivery to a weak‐grid/standalone load from a variable speed‐wind energy conversion system (VS‐WECS). The proposed system consists of a permanent magnet synchronous generator (PMSG)‐based WECS connected to a weak‐grid/standalone through a direct matrix converter‐based solid‐state transformer (SST) along with a bidirectional buck–boost battery energy storage system (BESS). The power from the WECS is processed through the SST using Venturini‐based control algorithm together with the voltage‐oriented control scheme. This scheme effectively regulates active and reactive power as well as voltage gain of the SST. Wind turbines maximum power point tracking power curves are used to set reference for active power. While the reference for reactive power is taken as zero. The bidirectional buck–boost‐based BESS is used to smoothen the power generated by the PMSG‐based VS‐WECS. It absorbs the surplus power from the WECS during over generating period and delivers power back under power shortage period. To validate the performances of the proposed scheme, extensive simulation has been performed and exclusive hardware built by dSPACE1104 using MATLAB/Simulink.

In this study, the effect of wind on the mass suction ratio in case of a straight louvered infra-red suppression device (IRS) has been studied using numerical simulations. Two types of configurations were studied. In the first configuration, the louvers were placed symmetrically to each other w.r.t to the central axis of the funnel. The diameter of the louvers, in this case, was kept as maximum as geometrically possible. For the second configuration, the diameter of the louvers was reduced to accommodate more space in between the louvers and angle between the adjacent louvers in each row was varied. The placement configuration was simulated for both symmetrical placement and asymmetrical placement and results are reported. The Reynolds number of the nozzle exit fluid has been varied from 3000 to 7000. The wind velocity has been considered up to 20 m/s. The variation of mass suction ratio due to changing Reynolds number and changing wind velocity is reported for all the configurations. It has been found that in case of cross flow, symmetrical placement of louvers provides maximum mass suction.

Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop growth and helping farmers make market decisions. Many parametric and non-parametric machine learning techniques have been utilized to estimate these parameters. A general non-parametric approach that follows a Bayesian framework is the Gaussian Process (GP). The parameters of this process-based technique are assumed to be random variables with a joint Gaussian distribution. The purpose of this work is to investigate Gaussian Process Regression (GPR) models to retrieve biophysical parameters of three annual crops utilizing combinations of multiple polarizations from C-band SAR data. RADARSAT-2 full-polarimetric images and in situ measurements of wheat, canola, and soybeans obtained from the SMAPVEX16 campaign over Manitoba, Canada, are used to evaluate the performance of these GPR models. The results from this research demonstrate that both the full-pol (HH+HV+VV) combination and the dual-pol (HV+VV) configuration can be used to estimate PAI, VWC, and WB for these three crops.

Crop characterization using Compact-Pol Synthetic Aperture Radar (CP-SAR) data is of prime interest with the rapid advancements of SAR systems towards operational applications. It is noteworthy that as a good compromise between the dual and quad-polarized SAR systems, the CP-SAR offer advantages in terms of the larger swath and lower data rate. The m − χ CP decomposition considers two out of the three Stokes child parameters: degree of polarization (m), ellipticity (χ), and orientation angle (ψ) to describe the polarized part of the quasi-monochromatic partially polarized wave. An improvement in the scattering powers was proposed in the S − Ω decomposition, which takes into accounts both the transmitted and received wave ellipticities (χt, χr) and the orientation angles (ψt, ψr). In this decomposition, S denotes the Stokes vector and Ω is the polarized power fraction. However, it may be noted that the S − Ω decomposition intrinsically ignores dominance in the target scattering mechanism while calculating the powers. In this work, improvement is proposed for the S − Ω decomposition by utilizing the degree of dominance in the scattering mechanism. The improved S − Ω (named as iS − Ω) decomposition powers are first compared with the existing m − χ and S − Ω powers for elementary (viz., trihedral and dihedral corner reflectors) and distributed targets using simulated CP-SAR data from quad-pol RADARSAT-2 data. An increase of ∼2% for odd and even-bounce powers obtained from the iS − Ω decomposition is observed for the trihedral and dihedral corner reflectors respectively. The analysis of the scattering powers for distributed targets shows that an increase of 15% and 12% in the even and odd-bounce powers is observed from iS − Ω for urban and bare soil areas respectively as compared to the m − χ and S − Ω decompositions. Besides, temporal variations in the scattering powers obtained from the iS − Ω decomposition are also analyzed for rice, cotton, and sugarcane crops at different growth stages.

Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.

Global crop mapping and monitoring requires high-resolution spatio-temporal information. In this regard, dual polarimetric Synthetic Aperture Radar (SAR) sensors provide high temporal and high spatial resolutions with large swath width. Generally, crop phenological development studies utilized SAR backscatter intensity-based descriptors. However, these descriptors are derived either from the covariance matrix elements or from the eigendecomposition. Therefore, this approach fails to utilize the complete polarization information of the scattered wave. In this study, we propose a target characterization parameter, θxP that utilizes the 2D Barakat degree of polarization and the elements of the covariance matrix. We also propose an unsupervised clustering scheme using θxP and the scattering entropy, HxP. We utilize time-series Sentinel-1 data of canola and wheat fields over a Canadian test site to show the sensitivity of θxP to the development of crop morphology at different phenological stages. During the initial growth stages, θxP values are low due to the low vegetation density. In contrast, at advanced phenological stages, we observe decreased values of θxP due to the appearance of complex canopy structure. Similarly, the effectiveness of the unsupervised HxP/θxP clustering plane is also evident from the temporal clustering plots. This innovative clustering framework is beneficial for the operational use of Sentinel-1 SAR data for agricultural applications.

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.