Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery

Castillejo-González, Isabel Luisa; López‐Granados, Francisca; García-Ferrer, Alfonso; Peña-Barragãn, J. M.; Jurado-Expósito, Montserrat; Orden, Manuel Sánchez de la; González-Audícana, María

doi:10.1016/j.compag.2009.06.004

Cited by 231 publications

(145 citation statements)

References 24 publications

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“…Except for its spectral characteristics, it can make full use of the spatial features of objects, which can, to some degrees, strengthen the advantages of GE imagery for its high spatial resolution and compensate its limitation of poor spectral information. This is consistent with some previous studies, which underlined the specific superiority of object-based methods over traditional pixel-based classifiers for mapping land use/cover [47][48][49]. Therefore, the spatial features of objects in target need to be utilized as more as possible when performing an object-based classification on GE imagery, in particular when the spectral features cannot identify the targets accurately.…”

Section: Potentials Analysis Of Ge Imagery For Land Use/cover Mappingsupporting

confidence: 90%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics. OPEN ACCESSRemote Sens. 2013, 5 6027

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Section: Potentials Analysis Of Ge Imagery For Land Use/cover Mappingsupporting

confidence: 90%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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“…To overcome the "salt-and-pepper" effect, object-based approaches have been increasingly implemented in remote-sensed image analysis [29,30]. Since object-based methods can avoid being affected by spectral similarity between different crops or crop mosaicking variability, techniques based on objects become a more useful approach to map crop types [31]. In addition to the reflectance signal captured by the sensors, textural features can correspond to crop consistency and canopy structure [32,33].…”

Section: Introductionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

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Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

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“…Furthermore, LC maps are also a valuable contribution in the development of maintain policies particularly for ecologically protected areas and the restoration of native environments, as well as the monitoring of desertification and land degradation in regions. 2 Remote sensing has been appropriate source for LC thematic mapping. 3 Accordingly image classification 4 is the most widely used for this purpose it is the most frequently applied approach in developing land use and land cover spatial distribution maps.…”

Section: Introductionmentioning

confidence: 99%

Land-Cover Classification for East Suez Canal Region Using Hyperspectral Eo-1 Data

Afify¹,

Sheta²,

Arafat³

et al. 2017

ECB

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Earth Observation (EO-1) data provides a highest spectral resolution to get spectral information of Earth's Surface targets within 242 spectral bands at 30 m spatial resolution. In this context, the main objective of this paper is to produce a land cover map using hyperspectral data acquired by EO-1 Hyperion instrument over one test site. Atmospheric correction on the hyperspectral data was performed using ENVI's Fast Line-of-sight Atmospheric Analysis of Spectral Hyper-cubes (FLAASH) module. Support Vector Machine (SVM) classification was implemented on the dominant elements to produce a land cover map for test site. SVM is carried out in this research to deal with the multi-class issue of Hyperion data. Classification using the kernel functions in classification made the classifier robust against the outliers. The Land Cover Classification System (LCCS) was used to know the land cover classes. The result showed high accuracy for land cover map with machine learning classifier like SVM using hyperspectral remote sensing data. The overall classification accuracy obtained was 97.85.

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Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery

Cited by 231 publications

References 24 publications

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Land-Cover Classification for East Suez Canal Region Using Hyperspectral Eo-1 Data

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