Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management

Castillejo-González, Isabel Luisa; Peña-Barragãn, J. M.; Jurado-Expósito, Montserrat; Mesas‐Carrascosa, Francisco Javier; López‐Granados, Francisca

doi:10.1016/j.eja.2014.05.009

Cited by 69 publications

(65 citation statements)

References 23 publications

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“…The mapping of weeds has been addressed by most previous works by detecting weeds at late growth stage (e.g. flowering) using piloted aircrafts or QuickBird satellite imagery (Castillejo-González et al, 2014;Gutiérrez et al, 2008), although the spatial resolution of these platforms is not 35 suitable for seedling detection (pixel size around 50 cm and 2.6 m for piloted aircrafts and QuickBird satellite, respectively). Nonetheless, a new aerial platform has recently joined the traditional ones, known as the Unmanned Aerial Vehicle (UAV) (Moranduzzo & Melgani, 2014).…”

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Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

Pérez-Ortiz

Peña

Gutiérrez

et al. 2016

Expert Systems with Applications

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151

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This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sunflower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post-emergence site-specific control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of different nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of different statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great influence for weed mapping in both sunflower and maize crops.

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confidence: 99%

Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

Pérez-Ortiz

Peña

Gutiérrez

et al. 2016

Expert Systems with Applications

Self Cite

151

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“…In land cover classification, OBIA, which has become common in the last decade, has proven to be superior to other methods of classification [101,139,155]. OBIA produced high classification accuracies in most studies which were based on Landsat images for different land cover types; however, OBIA'has limitations such as choosing the appropriate segmentation scale and dealing with different steps, which can be a source of variation if not properly handled [94].…”

Section: Best Practices For Landsat Land Cover Classificationmentioning

confidence: 99%

Developments in Landsat Land Cover Classification Methods: A Review

2017

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Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

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“…The computation speed of the inter-row weed detection algorithm was higher than the Hough transformation method. Castillejo-González et al [15] compared and tested the pixel-based and object-based techniques with different classification algorithms, for mapping the weed patches in the wheat fields using multi-spectral satellite image. The pixel-based classifications were applied to the wheat fields.…”

Section: Related Workmentioning

confidence: 99%

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

Prema¹,

Murugan²

2016

ELCVIA

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Weed management is the most significant process in the agricultural applications to improve the crop productivity rate and reduce the herbicide application cost. Existing weed detection techniques does not yield better performance due to the complex background and illumination variation. Hence, there arises a need for the development of effective weed identification technique. To overcome this drawback, this paper proposes a novel Angular Texture Pattern (ATP) Extraction Method for crop and weed discrimination using curvelet transformation. In our proposed work, Adaptive Median Filter (AMF) is used for filtering the impulse noise from the image. Plant image identification is performed using green pixel extraction and K-means clustering. Wrapping based Curvelet transform is applied to the plant image. Feature extraction is performed to extract the angular texture pattern of the plant image. Particle Swarm Optimization (PSO)-based Differential Evolution Feature Selection (DEFS) approach is applied to select the optimal features. Then, the selected features are learned and passed through an RVM-based classifier to find out the weed. Edge detection and contouring is performed to identify the weed in the plant image. The fuzzy rule-based approach is applied to detect the low, medium and high levels of the weed patchiness. From the experimental results, it is clearly observed that the accuracy of the proposed approach is higher than the existing Support Vector Machine (SVM) based approaches. The proposed approach achieves higher accuracy, sensitivity, specificity, Hausdorff distance, Jaccard distance and Dice distance.

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Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management

Cited by 69 publications

References 23 publications

Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

Developments in Landsat Land Cover Classification Methods: A Review

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

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