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Remote Sensing Applications: Society and Environment (23529385)38
Crop mapping, vital for informed decision-making in agricultural and food planning, relies on accurate and current information about the distribution of agronomic lands. Remote Sensing and Earth Observation technologies have emerged as indispensable tools, providing up-to-date data and images in diverse spatial and temporal resolutions, offering a practical and cost-effective alternative to traditional methods. This paper surveys over 400 publications spanning four decades, with a notable increase in studies after 2010, focusing on crop mapping and monitoring using remote sensing imagery. Categorizing these studies based on the type of remote sensing data utilized—optical, radar, or a combination thereof—it also delves into the diverse strategies employed, including attributes used, processing units, and classification algorithms. To date, there has not been a comprehensive review study specifically focused on crop mapping. This paper emphasizes the innovations and advancements in remote sensing technologies and their applications in crop mapping. It highlights the integration of cutting-edge deep learning techniques, the utilization of high-resolution satellite data, and the development of hybrid models that combine multiple data sources for enhanced accuracy. Furthermore, this review identifies emerging trends and future directions in the field, offering insights into the potential of new technologies and methodologies. Through this comprehensive overview of crop mapping studies published in reputable scientific journals between 1980 and 2024, we illuminate the dynamic landscape of this field and underscore the unique contributions of our review to the existing body of literature. © 2025 Elsevier B.V.
European Journal of Remote Sensing (22797254)58(1)
The Ministry of Agriculture-Jihad (MAJ) and the Iranian Space Agency (ISA) aim to accurately estimate the cultivated area of strategic crops and evaluate their annual yield through meticulous crop mapping. However, Iran lacks a comprehensive, integrated approach using remote sensing and machine learning for this purpose. This study addressed this gap by developing a versatile, user-friendly crop mapping framework for Iran, utilizing Landsat-8 time series data and classical machine learning algorithms. Marvdasht in Fars province was selected as the pilot area due to its high diversity of agricultural crop types and its status as a significant agricultural hub in Iran. Furthermore, the most widely used and flexible methods available in crop mapping studies such as decision tree (DT), random forest (RF), rotation forest (RoF), support vector machine (SVM), and dynamic time warping (DTW) were used in this study. The results showed that the DTW and RF methods outperformed others, achieving approximately 96% accuracy and improving overall accuracy by 8% in creating the crop map for the pilot area. Additionally, this study demonstrated the effectiveness of Landsat-8 bands 2 to 5 along with the normalized difference vegetation index (NDVI) in reliably identifying all crops in the region. The proposed framework shows promise for significantly advancing crop mapping practices in Iran. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
ISPRS International Journal of Geo-Information (22209964)12(11)
Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing synthetic aperture radar (SAR) and optical images can significantly enhance the accuracy of classification. However, a major challenge in this field is the limited availability of training data, which adversely affects the performance of classifiers. In agricultural regions, the dominant crops typically consist of one or two specific types, while other crops are scarce. Consequently, when collecting training samples to create a map of agricultural products, there is an abundance of samples from the dominant crops, forming the majority classes. Conversely, samples from other crops are scarce, representing the minority classes. Addressing this issue requires overcoming several challenges and weaknesses associated with the traditional data generation methods. These methods have been employed to tackle the imbalanced nature of training data. Nevertheless, they still face limitations in effectively handling minority classes. Overall, the issue of inadequate training data, particularly for minority classes, remains a hurdle that the traditional methods struggle to overcome. In this research, we explore the effectiveness of a conditional tabular generative adversarial network (CTGAN) as a synthetic data generation method based on a deep learning network, for addressing the challenge of limited training data for minority classes in crop classification using the fusion of SAR-optical data. Our findings demonstrate that the proposed method generates synthetic data with a higher quality, which can significantly increase the number of samples for minority classes, leading to a better performance of crop classifiers. For instance, according to the G-mean metric, we observed notable improvements in the performance of the XGBoost classifier of up to 5% for minority classes. Furthermore, the statistical characteristics of the synthetic data were similar to real data, demonstrating the fidelity of the generated samples. Thus, CTGAN can be employed as a solution for addressing the scarcity of training data for minority classes in crop classification using SAR–optical data. © 2023 by the authors.
Remote Sensing Letters (2150704X)13(12)pp. 1260-1270
Synergetic use of sensors for soil moisture retrieval is attracting considerable interest due to the different advantages of different sensors. Active, passive, and optic data integration could be a comprehensive solution for exploiting the advantages of different sensors aimed at preparing soil moisture maps. Typically, pixel-based methods are used for multi-sensor fusion. Since, different applications need different scales of soil moisture maps, pixel-based approaches are limited for this purpose. Object-based image analysis employing an image object instead of a pixel could help us to meet this need. This paper proposes a segment-based image fusion framework to evaluate the possibility of preparing a multi-scale soil moisture map through integrated Sentinel-1, Sentinel-2, and Soil Moisture Active Passive (SMAP) data. The results confirmed that the proposed methodology was able to improve soil moisture estimation in different scales up to 20% better compared to pixel-based fusion approach. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Khosravi, I.,
Razoumny, Y.,
Hatami afkoueieh, J.,
Alavipanah, S.K. International Journal of Image and Data Fusion (19479832)12(1)pp. 48-63
This paper proposed an extended rotation-based ensemble method for the classification of a multi-source optical-radar data. The proposed method was actually inspired by the rotation-based support vector machine ensemble (RoSVM) with several fundamental refinements. In the first modification, a least squares support vector machine was used rather than the support vector machine due to its higher speed. The second modification was to apply a Platt calibrated version instead of a classical non-probabilistic version in order to consider more suitable probabilities for the classes. In the third modification, a filter-based feature selection algorithm was used rather than a wrapper algorithm in order to further speed up the proposed method. In the final modification, instead of a classical majority voting, an objective majority voting, which has better performance and less ambiguity, was employed for fusing the single classifiers. Accordingly, the proposed method was entitled rotation calibrated least squares support vector machine (RoCLSSVM). Then, it was compared to other SVM-based versions and also the RoSVM. The results implied higher accuracy, efficiency and diversity of the RoCLSSVM than the RoSVM for the classification of the data set of this paper. Furthermore, the RoCLSSVM had lower sensitivity to the training size than the RoSVM. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Alizadeh, M.,
Zabihi, H.,
Rezaie, F.,
Asadzadeh, A.,
Wolf, I.D.,
Langat, P.K.,
Khosravi, I.,
Pour, A.B.,
Nataj, M.M.,
Pradhan, B. Remote Sensing (20724292)13(22)
Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Khosravi, I.,
Razoumny, Y.,
Hatami afkoueieh, J.,
Alavipanah, S.K. European Journal of Remote Sensing (22797254)54(1)pp. 310-317
The data classification of fully polarimetric synthetic aperture radar (PolSAR) is one of the favourite topics in the remote sensing community. To date, a wide variety of algorithms have been utilized for PolSAR data classification, and among them kernel methods are the most attractive algorithms for this purpose. The most famous kernel method, i.e., the support vector machine (SVM) has been widely used for PolSAR data classification. However, until now, no studies to classify PolSAR data have been carried out using certain extended SVM versions, such as the least squares support vector machine (LSSVM), relevance vector machine (RVM) and import vector machine (IVM). Therefore, this work has employed and compared these four kernel methods for the classification of three PolSAR data sets. These methods were compared in two groups: the SVM and LSSVM as non-probabilistic kernel methods vs. the RVM and IVM as probabilistic kernel methods. In general, the results demonstrated that the SVM was marginally better, more accurate and more stable than the probabilistic kernels. Furthermore, the LSSVM performed much faster than the probabilistic kernel methods and its associated version, the SVM, with comparable accuracy. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
International Journal of Remote Sensing (13665901)40(18)pp. 7221-7251
Combining optical and polarimetric synthetic aperture radar (PolSAR) earth observations offers a complementary data set with a significant number of spectral, textural, and polarimetric features for crop mapping and monitoring. Moreover, a temporal combination of both sources of information may lead to obtaining more reliable results compared to the use of single-time observations. In this paper, an operational framework based on the stacked generalization of random forest (RF), which efficiently employed bi-temporal observations of optical and radar data, was proposed for crop mapping. In the first step, various spectral, vegetation index, textural, and polarimetric features were extracted from both data sources and placed into several groups. Each group was classified separately using a single RF classifier. Then, several additional classification tasks were accomplished by another RF classifier. The earth observations used in this paper were collected by RapidEye satellites and the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system over an agricultural region near Winnipeg, Manitoba, Canada. The results confirmed that the proposed methodology was able to provide a higher overall accuracy and kappa coefficient than traditional stacking method, and also than all the individual RFs using each group. These accuracy metrics were also better than those of the RFs using the stacked features. Moreover, only the proposed methodology could achieve standard accuracy (F-score ≥85%) for all crop types in the study area. The visual comparison also demonstrated that the crop maps produced by the proposed methodology had more homogeneous, uniform appearances. Moreover, the mixed pixels of crop types, which abundantly existed in the traditional stacking and individual RFs̕ maps, were significantly eliminated. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (21511535)12(12)pp. 4766-4772
A large variety of algorithms have been proposed for hyperspectral data classification in the literature. Among them, classical forest methods, such as bagged tree, random forest, oblique random forest, and rotation forest, have received much attention, thanks to their efficiency. Recently, two filter-based forest methods, called balanced filter-based forest (BFF) and cost-sensitive filter-based forest (CFF), have been proposed for high-dimensional data classification. These methods also have the solutions for imbalanced data problem. In this paper, these two methods were examined and compared with the classical forest methods for the classification of several well-known imbalanced hyperspectral datasets. The results indicated higher efficiency and reliability of the filter-based forests as compared to the classical forests. Moreover, they were smaller and sparser ensembles than the competitors. Furthermore, these two forests (especially BFF) were generally more successful for the classification of minority classes in hyperspectral datasets. © 2008-2012 IEEE.
International Journal of Remote Sensing (13665901)39(8)pp. 2159-2176
Cropland classification using optical and full polarimetric synthetic aperture radar (PolSAR) images is a topic of considerable interest in the remote-sensing community. These two data sources can provide a diverse set of temporal, spectral, textural and polarimetric features which can be invaluable for cropland classification. However, some optical features or some radar features may have a relatively high correlation with other features. Hence, it seems to be necessary to choose the optimum features in order to reduce the dimensions of the data and to improve cropland classification accuracy. This article proposes a strategic feature selection method from a feature set of bitemporal RapidEye and Uninhabited Aerial Vehicle synthetic aperture radar (UAVSAR) images. The proposed method is designed to select the most relevant features and to remove redundant features based on the two concepts of separability and dependency. The proposed method is therefore referred to as maximum separability and minimum dependency (MSMD). For evaluating efficiency, MSMD and some well-known filter and wrapper feature selection methods are compared using a random forest classifier. Experimental tests confirmed that the classification results obtained from the MSMD feature selection method were more accurate than those achieved by filter methods. Moreover, they had an accuracy comparable to that of the results from the wrapper method. Furthermore, with regard to running time, MSMD operated as fast as the filter methods. It had a straightforward structure compared to the wrapper method, and as a result was faster than this method. © 2018 Informa UK Limited, trading as Taylor & Francis Group.
International Journal of Remote Sensing (13665901)39(21)pp. 7547-7567
The aim of this article is to improve land-cover classification accuracy from multifrequency full-polarimetric synthetic aperture radar (PolSAR) observations using multiple classifier systems (MCSs) when limited training samples are available. Two types of popular MCSs, tree-based MCSs and neural-based MCSs, were compared with individual decision tree (DT) and neural network methods. Moreover, an objective majority voting (OMV) was proposed and compared with majority voting (MV) and weighted MV (WMV) to fuse the results of the MCSs. Experimental tests were performed on three benchmark PolSAR data sets with different frequencies (X, C, and L) over the San Francisco Bay, CA. The results indicated (1) tree-based MCSs and neural-based MCSs, in general, produced higher overall, producerʼs and userʼs accuracies than the related individual methods, i.e. DT and NN, with limited training samples; (2) tree-based MCSs were also often more accurate and much faster than neural-based MCSs; (3) regarding robustness, among the MCSs, random forest showed higher stability while bagging showed lower stability in the classification of three PolSAR data sets; (4) the OMV proposed in this article usually outperformed its competitors, i.e. MV and WMV; (5) the results obtained by the methods from the C-band data set were more accurate and more reliable than those obtained from the X- and L-band data sets. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
Iranian Journal of Science and Technology - Transactions of Electrical Engineering (23641827)42(1)pp. 95-105
Although various building detection algorithms from high spatial resolution satellite (HSRS) images have been presented in recent years, there are yet some difficulties to detect building boundaries for mapping purposes. The present study aims to propose a new approach to detect building boundaries from HSRS images by focusing on higher detection rates. The approach utilizes the idea of object-based image processing. However, it has an innovative vision using image edges instead of traditional image segments as objects. To evaluate the efficiency of the proposed approach, two datasets which have different contrast between building and non-building areas, are used: the first dataset has a high contrast between building and non-building areas (HC) and second has a low contrast (LC). The results are compared with the results of two segmentation-based algorithms, i.e., classification based on edge-based segmentation (CBES) and classification based on multi-resolution segmentation (CBMS). The comparisons indicate higher efficiency of the proposed approach for the HC dataset with 6–14% higher detection rate (lower omission error) than the two segmentation-based algorithms. For the LC dataset, the proposed approach is already more efficient than CBMS with 10–25% higher detection rate. However, it has lower efficiency than CBES with 15–36% higher omission errors. Though, the proposed approach is generally more robust than CBES and CBMS algorithms based on standard deviation values of evaluation metrics. © 2018, Shiraz University.
International Journal of Remote Sensing (13665901)38(23)pp. 7138-7160
Fully polarimetric synthetic aperture radar (PolSAR) Earth Observations showed great potential for mapping and monitoring agro-environmental systems. Numerous polarimetric features can be extracted from these complex observations which may lead to improve accuracy of land-cover classification and object characterization. This article employed two well-known decision tree ensembles, i.e. bagged tree (BT) and random forest (RF), for land-cover mapping from PolSAR imagery. Moreover, two fast modified decision tree ensembles were proposed in this article, namely balanced filter-based forest (BFF) and cost-sensitive filter-based forest (CFF). These algorithms, designed based on the idea of RF, use a fast filter feature selection algorithms and two extended majority voting. They are also able to embed some solutions of imbalanced data problem into their structures. Three different PolSAR datasets, with imbalanced data, were used for evaluating efficiency of the proposed algorithms. The results indicated that all the tree ensembles have higher efficiency and reliability than the individual DT. Moreover, both proposed tree ensembles obtained higher mean overall accuracy (0.5–14% higher), producer’s accuracy (0.5–10% higher), and user’s accuracy (0.5–9% higher) than the classical tree ensembles, i.e. BT and RF. They were also much faster (e.g. 2–10 times) and more stable than their competitors for classification of these three datasets. In addition, unlike BT and RF, which obtained higher accuracy in large ensembles (i.e. the high number of DT), BFF and CFF can also be more efficient and reliable in smaller ensembles. Furthermore, the extended majority voting techniques could outperform the classical majority voting for decision fusion. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
Natural Hazards (15730840)87(3)pp. 1523-1523
In the initial online-first publication the name of author Yaser Jouybari-Moghaddam was given as Yaser Jouybari Moghaddam (without the hyphen) which may have given rise to confusion about what the last name is. This has now been corrected. © 2017, Springer Science+Business Media B.V.
Remote Sensing Letters (2150704X)8(12)pp. 1152-1161
Polarimetric Synthetic Aperture Radar (PolSAR) imagery can provide valuable observables at different frequencies for classification tasks. In this paper, we assessed separability rate of various polarimetric features in three frequencies of X-, C-, and L-bands. To this end, Jeffries–Matusita distance was firstly used to measure separability of each polarimetric feature in each frequency band. Random Forest classifier was then applied to map various land cover classes in study area. The classification outputs indicated that C-band results were better and more reliable than L-band results and L-band results were subsequently better than X-band results. These results were perfectly compatible with the results obtained by the separability analysis of multifrequency PolSAR features. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
Natural Hazards (15730840)87(3)pp. 1507-1522
This paper aims to employ and compare four methods of neural network (NN), support vector regression (SVR), least squares support vector regression (LSSVR) and adaptive neuro-fuzzy inference system (ANFIS) for modeling the time series behavior of the meteorological and the remotely sensed (RS) drought indices of the eastern district of Isfahan during 2000–2014. The data used in the paper are the normalized difference vegetation index (NDVI) and the land surface temperature time series of MODIS satellite and the rainfall time series of TRMM satellite. Then, three RS drought indices namely vegetation condition index, NDVI deviation index and temperature vegetation index and three meteorological drought indices namely 3-month SPI, 6-month SPI and 12-month SPI are generated by the data. Afterward, based on the correlation coefficient between the RS and the meteorological drought indices, three indices are chosen as candidate indices for monitoring the drought severity of the study area. After modeling the time series behavior of these indices by the aforementioned methods, the results indicate that the SVR has the highest and the NN has the lowest efficiency among all the methods. In addition, the performance speed of the LSSVR and then the ANFIS is the highest. At the end of the paper, a fuzzy inference system (FIS) is presented based on the candidate indices to monitor the drought severity at spring and summer of 2000–2014. According to the results of the designed FIS, the spring status is normal in all years except 2000 and 2011 (moderate drought) and the summer status is severe drought in all years except 2000, 2010, 2011 and 2014 (moderate drought). © 2017, Springer Science+Business Media Dordrecht.
Journal of the Indian Society of Remote Sensing (09743006)42(2)pp. 423-428
One of the most widely used outputs of remote sensing technology is Hyperspectral image. This large amount of information can increase classification accuracy. But at the same time, conventional classification techniques are facing the problem of statistical estimation in high-dimensional space. Recently in remote sensing, support vector machines (SVMs) have shown very suitable performance in classifying high dimensionality problem. Another strategy that has recently been used in remote sensing is multiple classifier system (MCS). It can also improve classification accuracy by combining different classifier methods or by a diversity of the same classifier. This paper aims to classify a Hyperspectral data using the most common methods of multiple classifier systems i.e. adaboost and bagging and a MCS based on SVM. The data used in the paper is an AVIRIS data with 224 spectral bands. The final results show the high capability of SVMs and MCSs in classifying high dimensionality data. © 2013 Indian Society of Remote Sensing.
Photogrammetric Engineering and Remote Sensing (00991112)80(6)pp. 519-528
This paper reviews and evaluates four building extraction algorithms including two pixel-based and two object-based methods using a diverse set of very high spatial resolution imagery. The applied images are chosen from different places (the cities of Isfahan, Tehran, and Ankara) and different sensors (QuickBird and GeoEye-1), which are diverse in terms of building shape, size, color, height, alignment, brightness, and density. The results indicate that the performance and the reliability of two object-based algorithms are better than pixel-based algorithms; about 10 percent to 15 percent better for the building detection rate and 6 percent to 10 percent better for the reliability rate. However, in some cases, the detection rate of pixel-based algorithms has been greater than 80 percent, which is a satisfactory result. On the other hand, segmentation errors can cause limitations and errors in the object-based algorithms, so that the commission error of object-based algorithms has been higher than pixel-based algorithms in some cases. © 2014 American Society for Photogrammetry and Remote Sensing.
