Knowledge acquisition and semantic rules discovery for CORINE land cover mapping using Latent Dirich

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Knowledge acquisition and semantic rules
discovery for CORINE land cover mapping using
Latent Dirichlet Allocation

• Sixth conference on Image Information Mining
• EUSC Torrejon Air Base
• 3-5 November 2009

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Land Use Land Cover Mapping

• Subject discussed at EU and international level
• CLC 2000, CLC 2006
• FAO LCCS
• GMES Soil Sealing, Forest, Urban Atlas
• GlobCover
• CLC methodology
• High resolution Visible IR data (Landsat, SPOT,
  IRS)
• False or natural color compositions
• Reference Topo 150K maps
• classification scheme
• Visual interpretation and delineation of polygons
• Quality control
• Products image data, vector layer, raster layer
  

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Difficulties in Land Use Land Cover Mapping

• Availability and price of EO data
• Resolution characteristics (spatial, spectral,
  temporal) not always / yet fulfilling user needs
• Landsat data successful in answering current
  requirements, no more available replaced by SPOT
  and IRS data
• New sensor data available not intensive use yet
  price is very high
• Methodology low productivity due to
• Most of the times visual interpretation and
  on-the-screen delineation of land cover polygons
  are the main tools
• Lack of operational automatic or semi-automatic
  methods capable of extracting the user defined
  classes / methods exists but are not used by the
  production unit
• Solutions pay for new sensor data and use
  automatic methods (i.e. stimulate research and
  pay for software) get data for low price and
  further invest in human operators and cheap
  software tools

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Difficulties in Land Use Land Cover Mapping

• Operational use of automatic or semi-automatic
  methods still not very widespread
• Sensors produce different data
• From one day to another
• From one place to another
• From one orbit to another, etc
• Data coming from different sensors is different
  (SPOTltgt IRSltgt RapidEye, ERSltgtRadarsat)
• Altered (processed data) can lead to different
  results (RAW data vs Radiometrically enhanced
  data)
• User defined classification schemes
  (nomenclatures) application specific not the
  same as the classes seen by the computer i.e.
  user not interested in a generic bare soil the
  user wants non irrigated arable land

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Current work in ROSA

• Many RD projects aiming to increase the
  effectiveness of extracting geo-information from
  EO data ROSAR, GEOINF, SIGUR, MUTER, ROKEO,
  SAFER, GEOLAND
• Both object oriented and pixel based methods are
  studied towards bridging the gap between high
  semantic, user defined needs and state-of-the art
  classification algorithms
• Results are tested and evaluated against existing
  LULC datasets produced and validated under
  operational conditions
• Algorithms are tested on original data and
  efforts are made to make them work on new sensor
  data

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From text to images - Latent Dirichlet Allocation

• Latent Dirichlet Allocation from text to image
  processing
• Probabilistic, Bayesian model focusing on
  documents i.e. subsets of pixels (tiles)
• Documents are made of words i.e. pixels and
  express a latent set of topics i.e classes
• The whole set of words define the vocabulary i.e.
  the dynamic range
• Each topic is modeled as a probability
  distribution over the vocabulary
• Many documents together form a corpus i.e. a
  satellite image
• The order of words in a document is ignored (bag
  of words)
• Based on training sets (histograms of documents),
  the model can discover the latent topics in
  documens

text corpus
vocabulary
document
topic
topic
topic
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Latent Dirichlet Allocation
Given a vocabulary with N visual words
For K topics, a two variables model can be derived
And then applied to image document, D
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LDA for CLC Mapping

• Due to complex semantics the (Document, Topics)
  couple is more close to the CLC classes i.e. a
  document can get a CLC class assigned based on
  latent topics inside it
• The huge vocabulary in a satellite image (dynamic
  range) can be reduced using unsupervised methods
  (e.g. k-means, SoilMapper) decrease processing
  time the new vocabulary is used to identify
  latent topics in a document
• The dimension of documents (tiles) function of
  the MMU (Minimum Mapping Unit 25 Ha) 15x15
  pixels for Landsat, 50x50 pixels for SPOT5
• Number of topics used depend on the CLC level
  addressed (5, 15, 44) and directly dependent on
  the unsupervised process e.g. 35 words for a
  Landsat image
• Based on latent topics, a document can get a
  label according to CLC nomenclature words -gt
  topics -gt classes
• Semantic rules can be derived for each of the
  classes in a nomenclature Class
  f(topics(words))

Satellite image
Compressed LANDSAT/ SPOT vocabulary
document
topic
topic
topic
CLC Class
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MEEO SoilMapper

• See Baraldi, A. et. al. 2006. Automatic Spectral
  Rule-Based Preliminary Mapping of Calibrated
  Landsat TM and ETM Images. IEEE Transactions on
  Geoscience and Remote Sensing, vol. 40, no.9
  (September 2006), 2563-2585
• Image data calibrated to reflectance values using
  sensor-specific information
• Output preliminary spectral map with pixel
  labeled according to an intermediate level
• For Landsat data 85, 41, 27 or 16 classes ca be
  derived
• Operational services provided by MEEO through ESA
  SSE

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CLC Mapping Workflow
Raw data reduced
vocabulary topics on pixels
topics on documents
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Tests results

• Tests were performed on Landsat data subsets of
  600x600 pixels and on SPOT data subsets of
  2000x2000 pixels, MEEO SoilMapper used for
  intermediate classification (vocabulary
  compression)

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Tests results

• Example 1 Landsat data 27 words / topics CLC
  Level 1 Classes
• Overall accuracy 92

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Tests results

• Example 2
• Landsat data
• 41 words / topics
• CLC Level 2 Classes
• Overall accuracy 70

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Semantic rules

• The current approach allows one to establish
  semantic rules for explaining high level semantic
  using intermediate level semantic

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Concluding remarks

• A method is proposed based on similarities that
  can be established between text and satellite
  images used in LULC mapping, based on words,
  documents and topics
• The current approach show promising results in
  trying to find solutions for bridging the gap
  between low level semantic data and high level
  semantic features by operating an intermediate
  level classification and then using LDA and thus
  addressing user requirements on providing
  meaningful information for policy making
• Currently, the availability of fully automatic
  methods allowing effective sensor specific
  intermediate level classification is exploited in
  order to reach a superior semantic level
• Although CLC tests were shown, any other high
  level semantic nomenclature can be applied
  semantic rules can be established between the
  intermediate and superior semantic level features
• A good level of accuracy is obtain by applying
  LDA on intermediate, fully automatic classified
  data
• In line with current discussions (GMES, INSPIRE)
  on new data models for LULC data promoting object
  oriented schemes

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Contact

• Dragos Bratasanu, ROSA dragos.bratasanu_at_rosa.ro
• Ion Nedelcu, ROSA ion.nedelcu_at_rosa.ro
• Mihai Datcu, DLR mihai.datcu_at_dlr.de