Bayoudh, Meriam

Nock, R.

Richard, G.

Roux, Emmanuel

Horizon / Pleins textes

Automatic Learning of Structural Knowledge from Geographic Information
for updating Land Cover Maps
Meriam Bayoudh1,2, Emmanuel Roux1, Richard Nock3, Gilles Richard4
1 ESPACE-DEV, UMR228 IRD/UMII/UR/UAG, Institut de Recherche pour le Développement, Centre IRD 
de Guyane, Route de Montabo BP165; 97323 Cayenne Cedex
firstname.name@ird.fr 
2 Université des Antilles et de la Guyane, Cayenne, French Guiana, France
3 CEREGMIA, UAG, Schoelcher, Martinique, France, richard.nock@martinique.univ-ag.fr 
4 IRIT, University of Toulouse, France, richard@irit.fr 
The  availability  of  remote  sensing  images  increases  due  to  the  multiplication  of  the  earth  observation 
satellites, the improvement of the image spatial and temporal resolutions and the changes in data distribution  
policies.  Such data quantities make the image processing and interpretation a new challenge for engineers  
and researchers. Therefore, new approaches have to be developed in order to automatically updating land  
cover/use  maps  that  provide useful  information  to  decision  makers.  The great  majority of  the  methods 
devoted to  satellite  image  supervised classification consider  pixel  information within the  image  regions  
associated to different classes, in order to learn class spectral signatures. Structural aspects within the pixel  
neighborhood are essentially considered by computing textural indexes within the same regions. Very few 
methods are based on structural knowledge discovery at a higher semantic level, like the one constituted by 
regions themselves, referred to as “objects” in the following. 
Our work consists in predicting the membership of the land cover/use classes by first inducing classification 
rules from existing land use/cover maps and from various complementary geographic information layers.  
More specifically, we propose to explicitly extract structural (relational) knowledge on the objects, in space 
and/or time.
We propose the application of the Inductive Logic Programming (ILP).  ILP is a learning approach which 
aims to induce rules from a set of examples and from background knowledge encoded in logic programs.  
The rules cover a maximum of positive examples and invalidate a maximum of negative examples. In our  
case, positive and negative examples are defined by the sets of objects which, respectively, belong and do  
not belong to the class of interest. Objects are described by attributes characterizing intrinsic features (class, 
area, perimeter, compactness, fractal dimension, crossed by a road/river) and relational features (inclusion,  
adjacency,  north/south,  east/west).  For  quantitative  variables,  each  of  the  object  values  is  re-coded  by 
indicating whether it is below or above the 10th, 20th, ... and 90th percentiles of the empirical distribution. 
As land cover/use typologies usually consist in several classes, the one-vs-rest induction method is applied,  
providing as many classifiers as there are classes.
The methodology was applied to update a land cover maps of the French Guiana littoral. We exploited land 
cover maps provided by the French Forestry Commission (Office National des Forêts – ONF). Such maps  
are based on a 39-class typology and are related to years 2001, 2005 and 2008. The hydrological and road 
networks were exploited as complementary geographical information.
343 classification rules were induced. A ten-fold cross validation of each classifier provided accuracy rate 
between 91.1% and 100.0% and an overall multi-model accuracy equal to 98.6%. However, these values  
greatly overestimate the overall system performances. In fact, the sensibility values vary from 0 to 100%, 
31% and 72% of the classifiers exhibiting sensibility values above 80% and 50%, respectively. As required,  
induced rules exhibit structural knowledge as shown with the following example:
   class_y(Object_A, Mangrove) :­ adjacent(Object_A, Object_B), 
class_y­7(Object_B, Flooded or swampy forest),  class_y­3(Object_A, Mangrove). 
Such rule means that an object A belongs to the class Mangrove at year y if it belonged to the same class 
three years before and is adjacent to an object  B which belonged to the class  Flooded or swampy 
forest 7 years before. The following example shows how numerical information coding has permitted to 
learn value intervals: 
  class08(Object_A,   Scattered   built)   :­   class_y­3(Object_A,   Scattered   built),  
fractal_dimension(Object_A, ≤321), fractal_dimension(Object_A, >243). 
Such rule means that an object A belongs to the class Scattered built if it belonged to the same class 
three years before and its fractal dimension is comprised between 243 and 321.
The application of the ILP to discover structural knowledge from maps provided simple and intelligible 
classification rules the predictive power of which appears very promising. Induced rules could be exploited 
to automatically classify new objects and/or  help operators using object-based classification procedures.


Automatic learning of structural knowledge from geographic information for updating land cover mapas

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