Classifiers vs. input variables—The drivers in image classification for land cover mapping

Heinl, Michael; Walde, Janette; Tappeiner, Gottfried; Tappeiner, Ulrike

doi:10.1016/j.jag.2009.08.002

Cited by 43 publications

(27 citation statements)

References 48 publications

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“…NN is also a nonparametric classifier with arbitrary decision boundary capabilities, easy adaptation to different data types and input structures as well as fuzzy output values and good generalization for use with multiple images [81]. It benefits from parallel computation, the ability to estimate the non-linear relationship between the input data and desired outputs, and fast generalization capability [82,83]. The parameter setting was based on experimental results.…”

Section: Determination Of the Land Cover Classification Schemementioning

confidence: 99%

Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

Attarchi

Gloaguen

2014

Remote Sensing

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Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand structures, which are difficult to discriminate. This paper explores the joint analysis of Landsat7/ETM+, L-band SAR and their derived parameters and the effect of terrain corrections to overcome the challenges of discriminating forest stand age classes in mountain regions. We also verified the performances of three machine learning methods which have recently shown promising results using multisource data; support vector machines (SVM), neural networks (NN), random forest (RF) and one traditional classifier (i.e., maximum likelihood classification (MLC)) as a benchmark. The non-topographically corrected ETM+ data failed to differentiate among different forest stand age classes (average classification accuracy (OA) = 65%). This confirms the need to reduce relief effects prior data classification in mountain regions. SAR backscattering alone cannot properly differentiate among different forest stand age classes (OA = 62%). However, textures and PolSAR features are very efficient for the separation of forest classes (OA = 82%). The highest classification accuracy was achieved by the joint usage of SAR and ETM+ (OA = 86%). However, this shows a slight improvement compared to the ETM+ classification (OA = 84%). The machine learning OPEN ACCESSRemote Sens. 2014, 6 3625 classifiers proved t o be more robust and accurate compared to MLC. SVM and RF statistically produced better classification results than NN in the exploitation of the considered multi-source data.

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Section: Determination Of the Land Cover Classification Schemementioning

confidence: 99%

Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

Attarchi

Gloaguen

2014

Remote Sensing

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“…The correct use of any supervised classifier requires that all classes that occur in the study area be included in the training stage of the analysis (Congalton and Green, 2008;Foody et al, 2006). Inappropriate class definition or missing input variables for discriminating classes will inevitably lead to inaccurate and blurred classification results (Heinl et al, 2009). The failure to exhaustively define classes can result in substantial errors which may also remain undetected in a subsequent classification accuracy assessment (Foody, 2002).…”

Section: Manuscriptmentioning

confidence: 99%

“…The exclusion classes in the training stage will typically result in cases of untrained classes being commissioned into the set of classes upon which the classifier was trained (Foody et al, 2006). The definition and selection of land cover classes, however, has shown to be crucial and not to be simply adaptable from existing land cover class schemes and a stronger focus must be put towards discriminating land cover classes by their typical spectral, topographic or seasonal properties (Heinl et al, 2009). The incorporation of a preliminary discrimination analysis, based on the actual image data to be used in the final classification process, requires knowledge of all present and potentially spectrally discernible classes in the study area and the availability of independent training and reference data for each class.…”

Section: Manuscriptmentioning

confidence: 99%

A proper Land Cover and Forest Type Classification Scheme for Mexico

Gebhardt

Maeda

Wehrmann

et al. 2015

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

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ABSTRACT:The imminent implementation of a REDD+ MRV system in Mexico in 2015, demanding operational annual land cover change reporting, requires highly accurate, annual and high resolution forest type maps; not only for monitoring but also to establish the historical baseline from the 1990s onwards. The employment of any supervised classifier demands exhaustive definition of land cover classes and the representation of all classes in the training stage. This paper reports the process of a data driven class separability analysis and the definition and application of a national land cover classification scheme. All Landsat data recorded over Mexico in the year 2000 with cloud coverage below 10 percent and a national digital elevation model have been used. Automatic wall-2-wall image classification has been performed trained by national reference data on land use and vegetation types with 66 classes. Validation has been performed against field plots of the national forest inventory. Groups of non-separable classes have subsequently been discerned by automatic iterative class aggregation. Class aggregations have finally been manually revised and modified towards a proposed national land cover classification scheme at 4 levels with 35 classes at the highest level including 13 classes for primary temperate and tropical forests, 2 classes for secondary temperate and tropical forest, 1 for induced or cultivated forest, as also 8 different scrubland classes. The remaining 11 classes cover agriculture, grassland, wetland, water bodies, urban and other vegetation land cover classes. The remaining 3 levels provide further hierarchic aggregations with 14, 10, and 8 classes, respectively. Trained by the relabeled training dataset wall-2-wall classification towards the 35 classes has been performed. The final national land cover dataset has been validated against more than 200,000 polygons randomly distributed all over the country with class labels derived by manual interpretation. The agreement for all 35 classes at level 4 was 71%. Primary forest classes have been identified with accuracies between 60% and 83%. Secondary forest classes rated only 50% finding major confusion with the primary forest classes. Accuracies over the scrubland classes have been calculated between 60% and 90%. Agreements for aggregated temperate and tropical forest classes was 85% and 80%, respectively. Separation of forest and non-forest has been achieved with an agreement of 87%.* Corresponding author MANUSCRIPTMexico has sported for the last 25 years national cartography featuring extremely high thematic resolution with more than 200 classes of vegetation types and land use types. The socalled INEGI (Instituto Nacional de Estadística y Geografía) series I-V have been giving the user an excellent overview of Mexico's main vegetation types at 5 to 10 years intervals (INEGI, 2005(INEGI, , 2008. While INEGI series are each in itself highly accurate in representing the vegetation and land use of Mexico, as a series they show serious limitations due to ...

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“…Detailed and accurate land cover data are widely used by various organizations, such as national, regional, local governments and private industries, as well as educational and research organizations because they are the basis for many environmental and socioeconomic applications (Perera and Tsuchiya, 2009;Heinl et al, 2009). The suitability of remote sensing for acquiring land cover data has long been recognised and land cover mapping with using satellite data has received growing attention in the last 20 years., but the process of generating land cover information from satellite data is still far from being standardised or optimised (Foody, 2002;Lu and Weng, 2007;Heinl et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…The suitability of remote sensing for acquiring land cover data has long been recognised and land cover mapping with using satellite data has received growing attention in the last 20 years., but the process of generating land cover information from satellite data is still far from being standardised or optimised (Foody, 2002;Lu and Weng, 2007;Heinl et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Large Area Land Cover Classification With Landsat Etm+ Images Based on Decision Tree

Zhai

Sun

Sang

et al. 2012

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

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Commission VII, WG VII/6: Remote Sensing Data Fusion KEY WORDS: land cover classification, decision tree, C5.0, MLC ABSTRACT:Traditional land classification techniques for large areas that use LANDSAT TM imagery are typically limited to the fixed spatial resolution of the sensors. For modeling habitat characteristics is often difficult when a study area is large and diverse and complete sampling of environmental variables is unrealistic. We also did some researches on this field, in this paper we firstly introduced the decision tree classification based on C5.0, and then introduced the classification workflow. The study results were compared with the Maximum Likelihood Classification result. Victoria of Australia was as the study area, the LANDSAT ETM+ images were used to classify. Experiments show that the decision tree classification method based on C5.0 is better.

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Classifiers vs. input variables—The drivers in image classification for land cover mapping

Cited by 43 publications

References 48 publications

Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest

A proper Land Cover and Forest Type Classification Scheme for Mexico

Large Area Land Cover Classification With Landsat Etm+ Images Based on Decision Tree

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