IGARSS2011-Villa.ppt

P. Villaa, M. Pepea, M. Boschettia, R. De PaulisbP. Villaa, M. Pepea, M. Boschettia, R. De Paulisb

a CNR-IREA, Institute for Electromagnetic Sensing of the Environment, Italyb ENI Exploration & Production Division – Remote Sensing Dept., Italy

13 April 2023

P. Villa, M. Pepe, M. Boschetti, R. De Paulis SPECTRAL MAPPING CAPABILITIES OF SEDIMENTARY ROCKS USING HYPERSPECTRAL DATA IN SICILY, ITALY 2

IGARSS 2011 - Vancouver

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

Introduction

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Outline

• Study Area and Dataset

• Hyperspectral Geology Mapping– Training Sample selection– Classification Algorithms

• Comparative Analysis of Results– Accuracy Assessment– Results Discussion

• Conclusions and Further Study

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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.

Study Area

SICILY

ITALY

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Dataset

Aerial hyperspectral sensor MIVIS (Multispectral Infrared and Visible Imaging Spectrometer)•92 bands (400-2400 nm, 10-20 nm of spectral resolution) •September 8th, 2008•Spatial resolution ranging from 1.6 to 3 m

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)

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Outline





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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).

Hyperspectral pre-processing: •atmospheric effect (MODTRAN radiative transfer code)•outcrops masking (Partial albedo and NDVI thresholding)

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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:

(1) Clay braccias (2) Marls and limestones (Trubi Formation)(3) Marly-diatomitic schists (Tripoli Formation)(4) Limestone, (5) Chalks (Pasquasia Formation)(6) Marlstone (Enna Formation)(7) Marlstone (Terravecchia Formation)

Geological Features of interest

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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.

Training Sample selection

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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)

Classification Algorithms

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Outline





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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 mapMAXLIKE 10% class

(training set Cartageo UNIPA)

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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.

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Results Discussion

The overall results are quite unsatisfactory in absolute terms, but broken down by class 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, •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

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).

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Outline





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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.

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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.

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P. Villaa, M. Pepea, M. Boschettia, R. De PaulisbP. Villaa, M. Pepea, M. Boschettia, R. De Paulisb

a CNR-IREA, Institute for Electromagnetic Sensing of the Environment, Italyb ENI Exploration & Production Division – Remote Sensing Dept., Italy

Send Questions to… Paolo Villa:

[email protected] Questions to… Paolo Villa:

[email protected]

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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)

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Additional Figures – Geological Maps

Carta Geologica -Affioramenti

Classificazione MAXLIKE 90% -Affioramenti (training set Cartageo UNIPA)

Classificazione MAXLIKE 10% -Affioramenti (training set Cartageo UNIPA)

Documents

IGARSS2011-Villa.ppt