Simulated multipolarized MAPSAR images to distinguish agricultural crops

Silva, Wagner Fernando; Rudorff, Bernardo Friedrich Theodor; Formaggio, Antônio Roberto; Paradellá, Waldir Renato; Mura, José Cláudio

doi:10.1590/s0103-90162012000300005

Cited by 6 publications

(2 citation statements)

References 22 publications

(31 reference statements)

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“…The achieved penetration depth depends on the biophysical parameter of the object which causes the spread in the plant layer (e.g., geometry, size and water content, scatter objects) that can increase or weaken the interaction between microwave and distribution-production characteristics [28]. SAR images have the potential not only to distinguish different crop types, but also to monitor crop growth [29,30]. Furthermore, to date, there is still a lack of health monitoring of the oil palm trees by using microwave wavelengths.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease

et al. 2021

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Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is to prolong the life of oil palm trees since there has been no effective treatment for the BSR disease. This project used an ALOS PALSAR-2 image with dual polarization, Horizontal transmit and Horizontal receive (HH) and Horizontal transmit and Vertical receive (HV). The aims of this study were to (1) identify the potential backscatter variables; and (2) examine the performance of machine learning (ML) classifiers (Multilayer Perceptron (MLP) and Random Forest (RF) to classify oil palm trees that are non-infected and infected by G. boninense. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approach (Synthetic Minority Over-Sampling Technique (SMOTE) in these classifications due to the differing sample sizes. The result showed backscatter variable HV had a higher correct classification for the G. boninense non-infected and infected oil palm trees for both classifiers; the MLP classifier model had a robust success rate, which correctly classified 100% for non-infected and 91.30% for infected G. boninense, and RF had a robust success rate, which correctly classified 94.11% for non-infected and 91.30% for infected G. boninense. In terms of model performance using the most significant variables, HV, the MLP model had a balanced accuracy (BCR) of 95.65% compared to 92.70% for the RF model. Comparison between the MLP model and RF model for the receiver operating characteristics (ROC) curve region, (AUC) gave a value of 0.92 and 0.95, respectively, for the MLP and RF models. Therefore, it can be concluded by using only the HV polarization, that both the MLP and RF can be used to predict BSR disease with a relatively high accuracy.

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Section: Introductionmentioning

confidence: 99%

Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease

et al. 2021

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“…With the launch of COSMO-SkyMed 1/2, RADARSAT-2 and Sentinel-1 satellites, multipolarization and fully polarimetric SAR (PolSAR) data have become available in recent years. PolSAR data contain information regarding the shape, dielectric constant, roughness, orientation and backscattering properties of ground objects, thereby having more advantages in crop classification and area monitoring [11][12][13] .…”

Section: Introduction mentioning

confidence: 99%

Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method

Cui

Zhang

Wang

et al. 2020

International Journal of Agricultural and Biological Engineering

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Remote sensing is an important technical means to investigate land resources. Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves, whereas synthetic aperture radar (SAR) imagery is sensitive to changes in growth states and morphological structures. Crop-type mapping with a single type of imagery sometimes has unsatisfactory precision, so providing precise spatiotemporal information on crop type at a local scale for agricultural applications is difficult. To explore the abilities of combining optical and SAR images and to solve the problem of inaccurate spatial information for land parcels, a new method is proposed in this paper to improve crop-type identification accuracy. Multifeatures were derived from the full polarimetric SAR data (GaoFen-3) and a high-resolution optical image (GaoFen-2), and the farmland parcels used as the basic for object-oriented classification were obtained from the GaoFen-2 image using optimal scale segmentation. A novel feature subset selection method based on within-class aggregation and between-class scatter (WA-BS) is proposed to extract the optimal feature subset. Finally, crop-type mapping was produced by a support vector machine (SVM) classifier. The results showed that the proposed method achieved good classification results with an overall accuracy of 89.50%, which is better than the crop classification results derived from SAR-based segmentation. Compared with the ReliefF, mRMR and LeastC feature selection algorithms, the WA-BS algorithm can effectively remove redundant features that are strongly correlated and obtain a high classification accuracy via the obtained optimal feature subset. This study shows that the accuracy of crop-type mapping in an area with multiple cropping patterns can be improved by the combination of optical and SAR remote sensing images.

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Semi-empirical SAR vegetation index for crop discrimination based on biomass in semi-arid region: A case study in Perambalur district, India

Krishnan,

Asaithambi

2023

Remote Sensing Applications: Society and Environment

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Simulated multipolarized MAPSAR images to distinguish agricultural crops

Cited by 6 publications

References 22 publications

Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease

Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease

Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method

Semi-empirical SAR vegetation index for crop discrimination based on biomass in semi-arid region: A case study in Perambalur district, India

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