The use of high‐resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco

Simonneaux, Vincent; Duchemin, B.; Helson, D.; Er‐Raki, Salah; Olioso, Albert; Chehbouni, Abdelghani

doi:10.1080/01431160701250390

Cited by 167 publications

(104 citation statements)

References 26 publications

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“…The identification of outdoor crops by using Landsat time series has been already addressed by many authors [22,23]. Recently, an innovative methodology, which combines multi-temporal moderate resolution remote sensing data, the OBIA approach and the decision tree (DT) classifier algorithm, has been proposed [24,25].…”

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confidence: 99%

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

et al. 2015

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Greenhouse detection and mapping via remote sensing is a complex task, which has already been addressed in numerous studies. In this research, the innovative goal relies on the identification of greenhouse horticultural crops that were growing under plastic coverings on 30 September 2013. To this end, object-based image analysis (OBIA) and a decision tree classifier (DT) were applied to a set consisting of eight Landsat 8 OLI images collected from May to November 2013. Moreover, a single WorldView-2 satellite image acquired on 30 September 2013, was also used as a data source. In this approach, basic spectral information, textural features and several vegetation indices (VIs) derived from Landsat 8 and WorldView-2 multi-temporal satellite data were computed on previously segmented image objects in order to identify four of the most popular autumn crops cultivated under greenhouse in Almería, Spain (i.e., tomato, pepper, cucumber and aubergine). The best classification accuracy (81.3% overall accuracy) was achieved by using the full set of Landsat 8 time series. These results were considered good in the case of tomato and pepper crops, being significantly worse for cucumber and aubergine. These OPEN ACCESSRemote Sens. 2015, 7 7379 results were hardly improved by adding the information of the WorldView-2 image. The most important information for correct classification of different crops under greenhouses was related to the greenhouse management practices and not the spectral properties of the crops themselves.

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Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

et al. 2015

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“…This approach does not take into account the temporal dependencies (Bruzone et al, 2004) (Waske and Braun, 2009). The second one is based on modelling temporal dependencies by rules (Simonneaux et al, 2008), or adaptive strategies to select the relevance of features over time for specific crops (Müller et al, 2010). The last one incorporates temporal dependencies into statistical models (Melgani and Serpico, 2004) (Leite et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Method to Estimate Temporal Interaction in a Conditional Random Field Based Approach for Crop Recognition

Diaz¹,

Feitosa²,

Sanches³

et al. 2016

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

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ABSTRACT:This paper presents a method to estimate the temporal interaction in a Conditional Random Field (CRF) based approach for crop recognition from multitemporal remote sensing image sequences. This approach models the phenology of different crop types as a CRF. Interaction potentials are assumed to depend only on the class labels of an image site at two consecutive epochs. In the proposed method, the estimation of temporal interaction parameters is considered as an optimization problem, whose goal is to find the transition matrix that maximizes the CRF performance, upon a set of labelled data. The objective functions underlying the optimization procedure can be formulated in terms of different accuracy metrics, such as overall and average class accuracy per crop or phenological stages. To validate the proposed approach, experiments were carried out upon a dataset consisting of 12 co-registered LANDSAT images of a region in southeast of Brazil. Pattern Search was used as the optimization algorithm. The experimental results demonstrated that the proposed method was able to substantially outperform estimates related to joint or conditional class transition probabilities, which rely on training samples.

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“…Regarding the number of classes, our findings were relevant in comparison to other remote sensing investigations that involved the classification of a smaller number of crops. For example, Conrad et al [46] classified only three irrigated crops (cotton, rice, wheat) with 80% overall accuracy, and Simonneaux et al [7] averaged 85% overall accuracy in the classification of three general land uses (annual crops, trees and bare soil). They also concluded that discrimination among different types of permanent crops (e.g., alfalfa, trees or woody crops) continue to represent major problems.…”

Section: Classification Performance Of Individual Cropsmentioning

confidence: 99%

“…In this topic, several authors have observed that information concerning variations in crop calendar, crop patterns, crop management techniques and parcel sizes shall be incorporated to the classifier algorithms for a successful result [6,7]. These variations can be derived from the textural, contextual or, in some cases, morphological features of the images [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Image Classification of Summer Crops with Machine Learning Methods

et al. 2014

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Abstract:The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the OPEN ACCESS Remote Sens. 2014, 6 5020 most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.

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The use of high‐resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco

Cited by 167 publications

References 26 publications

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain

A Method to Estimate Temporal Interaction in a Conditional Random Field Based Approach for Crop Recognition

Object-Based Image Classification of Summer Crops with Machine Learning Methods

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