P. Villa a , M. Pepe a , M. Boschetti a , R. De Paulis b a  CNR-IREA ,   Institute for Electromagnetic Sensing of the Envi...
Introduction Geological applications  in Remote Sensing have usually exploited supervised classification algorithms and ex...
Outline <ul><li>Study Area and Dataset </li></ul><ul><li>Hyperspectral Geology Mapping </li></ul><ul><ul><li>Training Samp...
Study Area Serra di Falco  site (4 km2), Caltanissetta basin, Sicily, southern Italy The  geology  is composed of quite  s...
Dataset <ul><li>Aerial hyperspectral sensor  MIVIS  (Multispectral Infrared and Visible Imaging Spectrometer) </li></ul><u...
Outline <ul><li>Study Area and Dataset </li></ul><ul><li>Hyperspectral Geology Mapping </li></ul><ul><ul><li>Training Samp...
Intro and Pre-processing Focus  of this work is on the  spectral mapping capabilities  of hyperspectral remotely sensed da...
Geological Features of interest <ul><li>The  geologic cover classes  covering the Serra di Falco area originated from a Ge...
Training Sample selection The  four types of training samples  for classification of the outcrops that have been  evaluate...
Classification Algorithms These  four  types of sampling were used for training  different supervised classifiers  in orde...
Outline <ul><li>Study Area and Dataset </li></ul><ul><li>Hyperspectral Geology Mapping </li></ul><ul><ul><li>Training Samp...
Geological mapping Starting from the  geological outcrops classification  using different approaches  (traing sampling/cla...
Accuracy assessment Accuracy of geological maps for each combination of classifier and training set tested (with  referenc...
Results Discussion <ul><li>The overall results are quite unsatisfactory in absolute terms, but broken down by class they c...
Outline <ul><li>Study Area and Dataset </li></ul><ul><li>Hyperspectral Geology Mapping </li></ul><ul><ul><li>Training Samp...
Conclusions Spectral mapping capabilities  of hyperspectral remotely sensed data for sedimentary rocks classification, has...
Further Study Forthcoming works center on techniques for  strongly reducing commission error , even when this means high o...
P. Villa a , M. Pepe a , M. Boschetti a , R. De Paulis b a  CNR-IREA ,   Institute for Electromagnetic Sensing of the Envi...
Additional Figures – Geological Maps Carta Geologica -Affioramenti  Classificazione SAM -Affioramenti (training set Camp. ...
Additional Figures – Geological Maps Carta Geologica -Affioramenti  Classificazione MAXLIKE 90% -Affioramenti (training se...
Upcoming SlideShare
Loading in …5
×

IGARSS2011-Villa.ppt

579 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
579
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Geological applications of hyperspectral Remote Sensing involving geo-lithologic mapping have usually exploited supervised classification algorithm fed with training samples from spectral data coming from laboratory or field work, external to the remote sensing images utilized (Chen et al., 2007; Hewson et al., 2005). This approach can raise some issues because of the difference and mismatching between spectra derived from remote sensing images (affected by geometric, radiometric and atmospheric induced distortions) and laboratory or field derived ones (Roy et al., 2009). This work, on the contrary, has chosen to focus on the spectral mapping capabilities of hyperspectral remotely sensed data for sedimentary rocks classification, making massive use of information coming from hyperspectral images only, and using supervised classifiers in a relative approach, thus limiting the use of ancillary data to geological classes definition and validation of mapping results. The objective of the study concerns the recognition and characterization of geological outcrops from MIVIS hyperspectral images, and to conduct a comparative analysis of automatic supervised classification from two point of view: the classification methodologies and the choice of spectral training samples (training set). This work aims at mapping a variety of geo-lithotypes in the study area of Serra di Falco, in Sicily, southern Italy, using different relative classification methodologies and different collecting strategies for training those classifiers, with the final target of comparing their performances and discussing their pro and cons in the context of geo-lithotyological mapping using hyperspectral aerial data coming from MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) sensor. For the purposes of the study aerial hyperspectral MIVIS data, acquired in September 2008, were processed and analyzed, using as a reference the geological maps produced by the University of Palermo. References: Xianfeng Chen, Timothy A. Warner, David J. Campagna, &amp;quot;Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada&amp;quot;, Remote Sensing of Environment, Volume 110, Issue 3, 15 October 2007, Pages 344-356 Hewson R.D., T.J. Cudahy, S. Mizuhiko, K. Ueda, A.J. Mauger, &amp;quot;Seamless geological map generation using ASTER in the Broken Hill-Curnamona province of Australia&amp;quot;, Remote Sensing of Environment, Volume 99, Issues 1-2, November 2005, Pages 159-172. Roy, R., Launeau, P., Carrère, V., Pinet, P., CeuleneerG., Clé net, H., Daydou, Y., Girardeau, J., Amri, I. &amp;quot;Geological mapping strategy using visible near-infrared-shortwave infrared hyperspectral remote sensing: Application to the Oman ophiolite (Sumail Massif)&amp;quot; (2009) Geochemistry, Geophysics, Geosystems, 10 (2)
  • The study area investigated is located in Caltanissetta goeological basin, Sicily, southern part of Italy. In particular, the focus of this work is on the Serra di Falco site, a small area measuring about 4 km2, where a significant set of geolithologic outcrops has been identified. The geology of the Caltanissetta basin, in which the study area is located, is composed of quite spectrally similar formations covering geo-lithological Gypsum-Sulphur series of evaporitic sedimentary rocks.
  • Remotely sensed data covering the study area were acquired by the aerial hyperspectral sensor MIVIS (Multispectral Infrared and Visible Imaging Spectrometer), held by CNR (LARA project). MIVIS data cover the ranges of Visible, Near Infrared, Shortwave Infrared and Thermal Infrared wavelenghts through 102 bands, of which 92 in the reflective part of the solar spectrum, 400-2400 nm, with 10-20 nm of spectral resolution. Aerial data were acquired on September 8th, 2008, at a spatial resolution ranging from 1.6 to 3 meters on the ground, depending on the flight altitude. The dataset used for Serra di Falco study area is composed of: • 3 runs of MIVIS data, orthoprojected in reference system WGS 84 UTM zone 33N, and in particular: 2 runs at 3 meters ground resolution and 1 run at 1.6 meters ground resolution; • A Geological map derived by the University of Palermo (Map A), Department of Geology and Geodesy, used as reference data (Scale 1:20,000); • A Geologic map of the outcrops derived by in situ campaign in 2008 by University of Palermo (Map B), Department of Geology and Geodesy.
  • The main focus of the work deals with the recognition and characterization capabilities of geolithologic outcrops using MIVIS hyperspectral images and remote sensing techniques only, and the analysis is therefore centered on a comparative assessment of supervised geologic classification methodologies with different classification algorithms and different approaches in the spectral samples selection to train the classifiers (training sets). MIVIS Hyperspectral data have been pre-processed through correction of atmospheric effect using MODTRAN radiative transfer code (Kaufman and Tanre, 1996) and all the features on the terrain not related to geological outcrops have been masked out based on albedo thresholding, to separate from the outcrops from other typologies of surface land cover, such as vegetated areas, shadow, or artificial surfaces (Feng et al., 2003). References: Kaufman, Y.J. and D. Tanre, 1996. &amp;quot;Strategy for Direct and Indirect Methods for Correcting the Aerosol Effect on Remote Sensing: from AVHRR to EOS-MODIS&amp;quot;. Remote Sensing of Environment 55:65-79. Feng Jilu, Benoit Rivard, Arturo Sanchez-Azofeifa, &amp;quot;The topographic normalization of hyperspectral data: implications for the selection of spectral end members and lithologic mapping&amp;quot;, Remote Sensing of Environment, Volume 85, Issue 2, 15 May 2003, Pages 221-231
  • The geologic cover classes covering the Serra di Falco area originated from a Geological Map derived from University of Palermo and express different geo-lithotypes characteristic of Caltanissetta geological basin Gypsum-Sulphur series from Messininan stage of Miocene to Pliocene, and in detail: Clay braccias Marls and limestones (Trubi Formation), Marly-diatomitic schists (Tripoli Formation), Limestone, Chalks (Pasquasia Formation), Marlstone (Enna Formation), Marlstone (Terravecchia Formation).
  • The four types of training samples for classification of the outcrops that have been evaluated in detail are represented by: • A Training set derived from field survey (Rilievo UNIPA) performed by University of Palermo (four geological classes with variable consistency between 40 and 62 pixels: Classes 2, 3, 4, 5; • A Training set derived from field survey performed by CNR-IREA (Campagna spec IREA) in connection with the acquisition of MIVIS data (four geological classes with variable consistency between 11 and 20 pixels: Classes 2, 3, 4, 6; • A Training set derived from an extension of the CNR-IREA field survey (Campagna spec SIMULATED), by integrating data covering missing classes from Geological Map of the area produced by the University of Palermo (five geological classes with variable consistency between 20 and 29 pixels: Classes 2, 3, 4, 5, 6; • A Training set resulting from the Geological Map derived from University of Palermo (Carta Geologica UNIPA), over the whole study area of Serra di Falco (seven geological classes with variable consistency between 227 and 332 pixels: Classes 1, 2, 3, 4, 5, 6, 7.
  • These four types of sampling were used for training different supervised classifiers in order to derive the mapping of geological outcrops in the area of interest. The four types of classifier chosen and used/tested in this study are: • Support Vector Machine (SVM): The SVM classifier derives from statistical learning applications and is based on the separation of classes of membership through a decision surface (linear or not) that maximize the separability between classes (Wu et al., 2004); • Spectral Angle Mapper (SAM): The SAM classifier exploits the n-dimensional angle formed by the spectral vector of values related to each pixel in the spectral data hyperspace in order to distinguish between different spectral classes. It is often used in geological applications because it is insensitive to changes in brightness, and effectively uses pure reflectance spectra as input for training (Kruse et al., 1993); • Spectral Information Divergence (SID): The SID is a clustering method that uses divergence measures (a mathematical operator) to derive the probability of every pixel in a multispectral image of belonging to a given class (Du et al., 2004); • Maximum Likelihood (MAXLIKE): The MAXLIKE classifier is a Bayesian statistical method that relies on the assumption that the target classes are normally distributed, using data from reference classes gathered as training samples to assign to each pixel in the image the constrained membership probability for each of the class distributions considered. Each pixel is then assigned the class with the highest membership probability, if this probability is over a minimum probability threshold set by the user (Richards, 1999). References: Wu, T.-F., C.-J. Lin, and R. C. Weng. (2004). &amp;quot;Probability estimates for multi-class classification by pairwise coupling.&amp;quot; Journal of Machine Learning Research, 5:975-1005 Kruse, F. A., A. B. Lefkoff, J. B. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz, 1993, “The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging spectrometer Data.” Remote Sensing of the Environment, v. 44, pp. 145-163 Du, H., C.-I. Chang, H. Ren, F.M. D&apos;Amico, J. O. Jensen, J., &amp;quot;New Hyperspectral Discrimination Measure for Spectral Characterization,&amp;quot; Optical Engineering, Vol. 43, No. 8, 2004, 1777-1786. Richards, J.A., 1999, Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, p. 240.
  • Focus of this work is on the spectral mapping capabilities of hyperspectral remotely sensed data for sedimentary rocks classification, making massive use of information coming from hyperspectral images only . This is made conducting a brief comparative analysis of automatic supervised classification with different classification algorithms and different approaches in the spectral samples selection to train the classifiers ( training sets ).
  • For each combination of classifier and training set tested the accuracy of outcrop geological maps produced were calculated with reference to the Geological Map derived from University of Palermo (Map A). The accuracy assessment, expressed in terms of Overall Accuracy and Kappa Coefficient and User Accuracy for every class, calculated on the error matrix for every combination of classifier/training set, are shown in the Table
  • The overall results are quite unsatisfactory in absolute terms, but broken down by class (see Table in previous slide) they can provide valuable guidance on which classes are most easily identifiable in the context of the Caltanissetta basin and the geological formations present in it, such as the Enna Marlstone and Trubi Formation, and, conversely, which classes are less easily spectrally mapped with remote sensing methods and data such as Tripoli and Limestone. Comparative assessment of the overall accuracy of the methods tested shows the difficulty of providing a unique tool that allows a fully reliable geolithotypes map of Serra di Falco area only with spectral features coming from remote sensing data (MAXLIKE classification with probability threshold of 90% has very good accuracy, but covers only a small fraction of the pixels to be classified). What can be drawn, however, is a series of maps obtained by different methods, which may become a tool of interpretation in the hands of an expert (geologist). For example, the combined use of two maps produced by MAXLIKE classifier and two different minimum probability thresholds (10% and 90%) provides two different views; one is loose and allowing classification of the whole image, presenting misclassifications, while the other is conservative showing large unclassified portions, even if classified areas are very reliable - that can help the geologist in the reconstruction of the boundaries/limits and the lithology in the area.
  • For example, the combined use of two maps produced by MAXLIKE classifier and two different minimum probability thresholds (10% and 90%) provides two different views; one is loose and allowing classification of the whole image, presenting misclassifications, while the other is conservative showing large unclassified portions, even if classified areas are very reliable - that can help the geologist in the reconstruction of the boundaries/limits and the lithology in the area. One part is going to prune the errors, the other to fill the gaps classification. Completeness and accuracy are in this case conflicting goals and that require the mediation of the expert, but can be efficient and synoptic ancillary data for geological mapping purposes. This is true especially from the point of view contextualization and spatialization (or interpolation) of some known results on the ground or collected from other data sources.
  • IGARSS2011-Villa.ppt

    1. 1. P. Villa a , M. Pepe a , M. Boschetti a , R. De Paulis b a CNR-IREA , Institute for Electromagnetic Sensing of the Environment, Italy b ENI Exploration & Production Division – Remote Sensing Dept., Italy Spectral mapping Capabilities of sedimentary Rocks using Hyperspectral Data in Sicily, Italy
    2. 2. Introduction Geological applications in Remote Sensing have usually exploited supervised classification algorithms and external training data (in situ, laboratory). Issues : difference and mismatching between spectra derived from remote sensing images (affected by geometric, radiometric and atmospheric induced distortions ) and laboratory or field derived ones. Focus of this work is on the spectral mapping capabilities of hyperspectral remotely sensed data for sedimentary rocks classification, making massive use of information coming from hyperspectral images only . The objective of the study concerns the recognition and characterization of geological outcrops from MIVIS hyperspectral images, and to conduct a brief comparative analysis of automatic supervised classification
    3. 3. Outline <ul><li>Study Area and Dataset </li></ul><ul><li>Hyperspectral Geology Mapping </li></ul><ul><ul><li>Training Sample selection </li></ul></ul><ul><ul><li>Classification Algorithms </li></ul></ul><ul><li>Comparative Analysis of Results </li></ul><ul><ul><li>Accuracy Assessment </li></ul></ul><ul><ul><li>Results Discussion </li></ul></ul><ul><li>Conclusions and Further Study </li></ul>
    4. 4. Study Area Serra di Falco site (4 km2), Caltanissetta basin, Sicily, southern Italy The geology is composed of quite spectrally similar formations covering geo-lithological Gypsum-Sulphur series of evaporitic sedimentary rocks. SICILY ITALY
    5. 5. Dataset <ul><li>Aerial hyperspectral sensor MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) </li></ul><ul><li>92 bands (400-2400 nm, 10-20 nm of spectral resolution) </li></ul><ul><li>September 8th, 2008 </li></ul><ul><li>Spatial resolution ranging from 1.6 to 3 m </li></ul>The dataset used for Serra di Falco study area is composed of: • 3 runs of MIVIS data; • A Geological map derived by the University of Palermo (Map A); • A Geologic map of the outcrops derived by in situ campaign in 2008 by University of Palermo (Map B)
    6. 6. Outline <ul><li>Study Area and Dataset </li></ul><ul><li>Hyperspectral Geology Mapping </li></ul><ul><ul><li>Training Sample selection </li></ul></ul><ul><ul><li>Classification Algorithms </li></ul></ul><ul><li>Comparative Analysis of Results </li></ul><ul><ul><li>Accuracy Assessment </li></ul></ul><ul><ul><li>Results Discussion </li></ul></ul><ul><li>Conclusions and Further Study </li></ul>
    7. 7. Intro and Pre-processing Focus of this work is on the spectral mapping capabilities of hyperspectral remotely sensed data for sedimentary rocks classification, making massive use of information coming from hyperspectral images only . This is made conducting a brief comparative analysis of automatic supervised classification with different classification algorithms and different approaches in the spectral samples selection to train the classifiers ( training sets ). <ul><li>Hyperspectral pre-processing : </li></ul><ul><li>atmospheric effect (MODTRAN radiative transfer code) </li></ul><ul><li>outcrops masking (Partial albedo and NDVI thresholding) </li></ul>
    8. 8. Geological Features of interest <ul><li>The geologic cover classes covering the Serra di Falco area originated from a Geological Map derived from University of Palermo and express different geo-lithotypes characteristic of Caltanissetta geological basin Gypsum-Sulphur series from Messininan stage of Miocene to Pliocene, and in detail: </li></ul><ul><li>Clay braccias </li></ul><ul><li>Marls and limestones (Trubi Formation) </li></ul><ul><li>Marly-diatomitic schists (Tripoli Formation) </li></ul><ul><li>Limestone , </li></ul><ul><li>Chalks (Pasquasia Formation) </li></ul><ul><li>Marlstone (Enna Formation) </li></ul><ul><li>Marlstone (Terravecchia Formation) </li></ul>
    9. 9. Training Sample selection The four types of training samples for classification of the outcrops that have been evaluated in detail are represented by: • A Training set derived from field survey (Rilievo UNIPA) performed by University of Palermo (four geological classes with variable consistency between 40 and 62 pixels: Classes 2, 3, 4, 5 ; • A Training set derived from field survey performed by CNR-IREA (Campagna spec IREA) in connection with the acquisition of MIVIS data (four geological classes with variable consistency between 11 and 20 pixels: Classes 2, 3, 4, 6 ; • A Training set derived from an extension of the CNR-IREA field survey (Campagna spec SIMULATED), by integrating data covering missing classes (five geological classes with variable consistency between 20 and 29 pixels: Classes 2, 3, 4, 5, 6 ; • A Training set resulting from the Geological Map derived from University of Palermo (Carta Geologica UNIPA), over the whole study area of Serra di Falco (seven geological classes with variable consistency between 227 and 332 pixels: Classes 1, 2, 3, 4, 5, 6, 7 .
    10. 10. Classification Algorithms These four types of sampling were used for training different supervised classifiers in order to derive the mapping of geological outcrops in the area of interest. The four types of classifier chosen and used/tested in this study are: • Support Vector Machine (SVM) • Spectral Angle Mapper (SAM) • Spectral Information Divergence (SID) • Maximum Likelihood (MAXLIKE)
    11. 11. Outline <ul><li>Study Area and Dataset </li></ul><ul><li>Hyperspectral Geology Mapping </li></ul><ul><ul><li>Training Sample selection </li></ul></ul><ul><ul><li>Classification Algorithms </li></ul></ul><ul><li>Comparative Analysis of Results </li></ul><ul><ul><li>Accuracy Assessment </li></ul></ul><ul><ul><li>Results Discussion </li></ul></ul><ul><li>Conclusions and Further Study </li></ul>
    12. 12. Geological mapping Starting from the geological outcrops classification using different approaches (traing sampling/classification algorithm) a brief comparative analysis of results was carried out. MIVIS data (CIR visualisation) Reference geological map MAXLIKE 10% class (training set Cartageo UNIPA)
    13. 13. Accuracy assessment Accuracy of geological maps for each combination of classifier and training set tested (with reference to the Geological Map derived from University of Palermo). The accuracy assessment was expressed in terms of Overall Accuracy for every combination classifier/training and in terms of class User Accuracy for best resulting combinations .
    14. 14. Results Discussion <ul><li>The overall results are quite unsatisfactory in absolute terms, but broken down by class they can provide valuable guidance on: </li></ul><ul><li>which classes are most easily identifiable in the context of the Caltanissetta basin and the geological formations present in it, such as the Enna Marlstone and Trubi Formation , </li></ul><ul><li>which classes are less easily spectrally mapped with remote sensing methods and data such as Tripoli and Limestone . </li></ul><ul><li>Comparative assessment of the overall accuracy of the methods tested shows the difficulty of providing a unique tool that allows a fully reliable geolithotypes map of Serra di Falco area only with spectral features coming from remote sensing data </li></ul><ul><li>What can be drawn, however, is a series of maps obtained by different methods, which may become a tool of interpretation in the hands of an expert (geologist). </li></ul>
    15. 15. Outline <ul><li>Study Area and Dataset </li></ul><ul><li>Hyperspectral Geology Mapping </li></ul><ul><ul><li>Training Sample selection </li></ul></ul><ul><ul><li>Classification Algorithms </li></ul></ul><ul><li>Comparative Analysis of Results </li></ul><ul><ul><li>Accuracy Assessment </li></ul></ul><ul><ul><li>Results Discussion </li></ul></ul><ul><li>Conclusions and Further Study </li></ul>
    16. 16. Conclusions Spectral mapping capabilities of hyperspectral remotely sensed data for sedimentary rocks classification, has proven not sufficient for general geological mapping , but an insight of results suggest that maps produced can be anyway useful . Completeness and accuracy are in this case conflicting goals and that require the mediation of the expert , but can be efficient and synoptic ancillary data for geological mapping purposes. For example, the combined use of two maps produced by MAXLIKE classifier and two different minimum probability thresholds (10% and 90%) provides two different views; one is loose and allowing classification of the whole image, presenting misclassifications, while the other is conservative showing large unclassified portions, even if classified areas are very reliable - that can help the geologist in the reconstruction of the boundaries/limits and the lithology in the area.
    17. 17. Further Study Forthcoming works center on techniques for strongly reducing commission error , even when this means high omission and large parts of the data left unclassified, for building a source of reliable seeds for region growing techniques which exploits contextual (DTM, other data) and textural information as well as spectral ones to derive a more accurate geological outcrops mapping.
    18. 18. P. Villa a , M. Pepe a , M. Boschetti a , R. De Paulis b a CNR-IREA , Institute for Electromagnetic Sensing of the Environment, Italy b ENI Exploration & Production Division – Remote Sensing Dept., Italy Spectral mapping Capabilities of sedimentary Rocks using Hyperspectral Data in Sicily, Italy Send Questions to… Paolo Villa: [email_address]
    19. 19. Additional Figures – Geological Maps Carta Geologica -Affioramenti Classificazione SAM -Affioramenti (training set Camp. IREA) Classificazione SVM -Affioramenti (training set Camp. SIMULATA) Classificazione SVM -Affioramenti (training set Cartageo UNIPA)
    20. 20. Additional Figures – Geological Maps Carta Geologica -Affioramenti Classificazione MAXLIKE 90% -Affioramenti (training set Cartageo UNIPA) Classificazione MAXLIKE 10% -Affioramenti (training set Cartageo UNIPA)

    ×