A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

Tamiminia, Haifa; Homayouni, Saeid; McNairn, Heather; Safari, Abdolreza

doi:10.1016/j.jag.2017.02.010

Cited by 32 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, (α = 45 • ) denotes volume scattering. Anisotropy is useful in differentiating the scattering mechanisms and describing the relative importance of the second and third eigenvalues [9,11,39].…”

Section: Polarimetric Target Decompositionmentioning

confidence: 99%

“…The helix scattering term is particularly useful for man-made targets, because it generally exists in complex urban areas and then disappears for natural distributed scatterers. This decomposition model can also be applied to general scattering cases (not just man-made targets), since it incorporates a modification for the volume scattering matrix by changing the probability density function, and it automatically includes the reflection symmetry condition in non-reflection symmetric cases [9,13,41].…”

Section: Polarimetric Target Decompositionmentioning

confidence: 99%

See 1 more Smart Citation

Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

Üstüner

Şanlı

2019

IJGI

View full text Add to dashboard Cite

In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification. Besides the polarimetric target decomposed parameters, the original polarimetric features (linear backscatter coefficients, coherency, and covariance matrices) were also incorporated for crop classification. The experimental results demonstrated that polarimetric target decompositions, with the exception of Cloude–Pottier, were found to be superior to the original features in terms of overall classification accuracy. The highest classification accuracy (92.07%) was achieved by Yamaguchi, whereas the lowest (75.99%) was achieved by the covariance matrix. Model-based decompositions achieved higher performance with respect to eigenvector-based decompositions in terms of class-based accuracies. Furthermore, the results emphasize the added benefits of model-based decompositions for crop classification using PolSAR data.

show abstract

Section: Polarimetric Target Decompositionmentioning

confidence: 99%

Section: Polarimetric Target Decompositionmentioning

confidence: 99%

Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

Üstüner

Şanlı

2019

IJGI

View full text Add to dashboard Cite

show abstract

“…In the framework of SMAPVEX12, the polarimetric UAVSAR image acquisitions covered 14 dates between June 17 and July 17, 2012. In this study, the multi-look product (MLC) of flight line #31606 with spatial resolution of 5.0 m in range and 7.16 m in azimuth is used (Tamiminia, Homayouni et al 2017). This flight line covers all the investigated agricultural fields.…”

Section: Uavsar Time Seriesmentioning

confidence: 99%

Comparison of Decomposition Methods Over Agricultural Fields Using the Uavsar Polarimetric Synthetic Aperture Radar

Kiana

Homayouni

Sharifi

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

This paper investigates and compares the potential of five model-based polarimetric decompositions, namely those developed by Yamaguchi four-component decomposition), An & Yang3 and An & Yang4 for crop biomass detection over agricultural fields covered by various crops. The time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data and the ground truth of soil and vegetation characteristics collected during the Soil Moisture Active Passive (SMAP) Validation Experiment in 2012(SMAPVEX12) were used to compare the five decomposition methods with related to the scattering mechanisms and the biomass retrieval performances. The results show that the performance of each decomposition method for biomass retrieval depends on the crop types and the crop phenological stages. Finally, an overall biomass underestimation was observed from the five decompositions, and the highest regression value of 99% was obtained from Freeman decomposition as a result of the enhanced volume scattering. Indeed, Freeman-Durden model provided the best results.

show abstract

“…Generally, most crops have a growing season of several months. In this short time, different crops show distinct variations in terms of their geometric shape, structure, and dielectric properties with evolution of the phenological period [ 28 ]. Therefore, multitemporal SAR imagery has often been employed to improve crop discrimination capability and classification results.…”

Section: Introductionmentioning

confidence: 99%

Dryland Crop Classification Combining Multitype Features and Multitemporal Quad-Polarimetric RADARSAT-2 Imagery in Hebei Plain, China

Wang

Liu

Yan

et al. 2021

Sensors

View full text Add to dashboard Cite

The accuracy of dryland crop classification using satellite-based synthetic aperture radar (SAR) data is often unsatisfactory owing to the similar dielectric properties that exist between the crops and their surroundings. The main objective of this study was to improve the accuracy of dryland crop (maize and cotton) classification by combining multitype features and multitemporal polarimetric SAR (PolSAR) images in Hebei plain, China. Three quad-polarimetric RADARSAT-2 scenes were acquired between July and September 2018, from which 117 features were extracted using the Cloude–Pottier, Freeman–Durden, Yamaguchi, and multiple-component polarization decomposition methods, together with two polarization matrices (i.e., the coherency matrix and the covariance matrix). Random forest (RF) and support vector machine (SVM) algorithms were used for classification of dryland crops and other land-cover types in this study. The accuracy of dryland crop classification using various single features and their combinations was compared for different imagery acquisition dates, and the performance of the two algorithms was evaluated quantitatively. The importance of all investigated features was assessed using the RF algorithm to optimize the features used and the imagery acquisition date for dryland crop classification. Results showed that the accuracy of dryland crop classification increases with evolution of the phenological period. In comparison with SVM, the RF algorithm showed better performance for dryland crop classification when using full polarimetric RADARSAT-2 data. Dryland crop classification accuracy was not improved substantially when using only backscattering intensity features or polarization decomposition parameters extracted from a single-date image. Satisfactory classification accuracy was achieved using 11 optimized features (derived from the Cloude–Pottier decomposition and the coherency matrix) from 2 RADARSAT-2 images (acquisition dates corresponding to the middle and late stages of dryland crop growth). This study provides an important reference for timely and accurate classification of dryland crop in Hebei plain, China.

show abstract

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

Cited by 32 publications

References 27 publications

Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

Comparison of Decomposition Methods Over Agricultural Fields Using the Uavsar Polarimetric Synthetic Aperture Radar

Dryland Crop Classification Combining Multitype Features and Multitemporal Quad-Polarimetric RADARSAT-2 Imagery in Hebei Plain, China

Contact Info

Product

Resources

About