Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy

Pignatti, Stefano; Cavalli, Rosa Maria; Cuomo, V.; Fusilli, Lorenzo; Pascucci, Simone; Poscolieri, M.; Santini, Federico

doi:10.1016/j.rse.2008.11.006

Cited by 80 publications

(50 citation statements)

References 69 publications

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“…Unlike the overall accuracy that is calculated along the contingency matrix diagonal, the kappa coefficient takes into consideration the entire contingency matrix instead. The overall accuracy is the ratio between all validation pixels correctly classified (the total correct pixels) and validation pixels (the total number of pixels in the error matrix), whereas the user's accuracy includes commission errors and the producer's accuracy includes omission errors related to the individual classes [3,[96][97][98].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

2014

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Abstract:The mineral ore potential of many mountainous regions of the world, like the Kurdistan region of Iraq, remains unexplored. For logistical and sometimes political reasons, these areas are difficult to map using traditional methods. We highlight the improvement in remote sensing geological mapping that arises from the integration of geomorphic features in classifications. The Mawat Ophiolite Complex (MOC) is located in the NE of Iraq and is known for its mineral deposits. The aims of this study are: (I) to refine the existing lithological map of the MOC; (II) to identify the best discriminatory datasets for lithological classification, including geomorphic features and textures; and (III) to identify potential locations with high concentrations of chromite. We performed a Support Vector Machine (SVM) classification method to allow the joint use of geomorphic features, textures and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The updated map allowed the identification of a new mafic body and a substantial improvement of the geometry of the known lithological units. The use of geomorphic features allowed for the increase of the overall accuracy from 73% to 79.3%. In addition, we detected chromite occurrences within the ophiolite by applying Spectral Angle Mapping (SAM) technique. We identified two new locations having high concentrations of chromite and verified one of these promising areas and has coarsely crystalline chromite within dunite host rock. The chromium (Cr 2 O 3 ) concentration is ~8.46%. The SAM and SVM methods applied on ASTER satellite data show that these can be used as a powerful tool to explore ore deposits and to further improve lithological mapping in mountainous semi-arid regions.

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Section: Accuracy Assessmentmentioning

confidence: 99%

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

2014

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“…Previous works [e.g. Pignatti et al, 2009;Petropoulos et al, 2012;George et al, 2014] have highlighted the usefulness of hyperspectral satellite imagery to improve forest cover classification. However, the experimental trials have resulted in low classification accuracy (even lower than 70% in some cases), not suitable for operative uses.…”

Section: Discussionmentioning

confidence: 99%

“…A variety of classification approaches has been applied to remotely sensed hyperspectral data [Lu and Weng, 2007]: Spectral Angle Mapper [Vyas et al, 2011], Linear Discriminant Analysis [Clark et al, 2005], Decision Tree Classifier [Lawrence et al, 2004], Artificial Neural Networks [Erbek et al, 2004], Support Vector Machine [Dalponte et al, 2009] and Random Forest [Chan and Palinckx, 2008] are some of the advanced methods for hyperspectral data classification. Recently, Hyperion imagery data was used to map LULC in the Mediterranean context [Pignatti et al, 2009;Petropoulos et al, 2012]. Despite their interesting results, the main limitation of these experimental studies is due to the little training and validation subsets, entailing the inability to extend their findings on wider areas.…”

Section: Introductionmentioning

confidence: 99%

Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests

Puletti

Camarretta

Corona

2016

European Journal of Remote Sensing

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The objective of the present study is the comparison of the combined use of Earth Observation-1 (EO-1) Hyperion Hyperspectral images with the Random Forest (RF), Support Vector Machines (SVM) and Multivariate Adaptive Regression Splines (MARS) classifiers for discriminating forest cover groups, namely broadleaved and coniferous forests. Statistics derived from classification confusion matrix were used to assess the accuracy of the derived thematic maps. We demonstrated that Hyperion data can be effectively used to obtain rapid and accurate large-scale mapping of main forest types (conifers-broadleaved). We also verified higher capability of Hyperion imagery with respect to Landsat data to such an end. Results demonstrate the ability of the three tested classification methods, with small improvements given by SVM in terms of overall accuracy and kappa statistic.

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“…Traditional pixelbased classification approaches are limited as regards the analysis of heterogeneous landscapes and lead to the reported 'salt and pepper' results (Aplin et al, 1999;Lu and Weng, 2007). Therefore, the ML classifier needs more training data to characterize the classes than the other methods (Pignatti et al, 2009). …”

Section: Analysis Of Land Cover Classification In Arid Environment: Amentioning

confidence: 99%

Analysis of Land Cover Classification in Arid Environment: A Comparison Performance of Four Classifiers

Mustapha¹,

Lim²,

MatJafri³

2012

Remote Sensing of Planet Earth

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Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy

Cited by 80 publications

References 69 publications

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests

Analysis of Land Cover Classification in Arid Environment: A Comparison Performance of Four Classifiers

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