Istanbul 2004 
with rational 
002 Annual 
, April 19-26 
Jata Products 
Centre. 
' Handbook 
2. 
an integrated 
Sensors and 
14th EARSel 
panese Space 
the “Instituto 
  
RESTITUTION OF INFORMATION UNDER SHADOW IN REMOTE SENSING HIGH SPACE 
RESOLUTION IMAGES: APPLICATION TO IKONOS DATA OF SHERBROOKE CITY 
; Amani Massalabi, Dong-Chen He, Goze B. Bénié et Eric Beaudry 
Equipe ESTRITEL, Centre d'applications et de recherches en télédétection (CARTEL). 
Université de Sherbrooke 
Sherbrooke, QC, JIK 2R1 
E-mail : Massalabi.Amani(@Usherbrooke.ca 
KEY WORDS: Restitution, Spatial High Resolution, Urban, shadow, urban, Ikonos. 
ABSTRACT: 
Shadow regions are very present in the high spatial resolution images, particularly in urban environment. Shades have negative effects. 
They strongly disturb the classical techniques of image analysis by modifying surface appearance and sometimes involve loss of 
information under the surface they covered. In this study, we try to restore the information masked under the shadow by finding the types 
of surface covered by the shade. This search is based on the analysis of contextual, spectral and textural information from the shadow and 
its vicinity.This restitution supposes that shades are well identified, which was the aim of our former work. It takes into account the space 
configuration of the vicinity between the objects located sun side and surfaces in shade side which are supposed to receive the shadow. 
The application on an IKONOS image of Sherbrooke show that it is indeed possible to restore the information laying under the shadow 
and even discriminate several types of surface under the same shade, such as shades of buildings which are projected at the same time on 
grass, a portion of parking or on another building. The method also makes it possible to correct the shadow effects on the images. 
1. INTRODUCTION 
The advent of the sensors with very high space resolution 
(Ikonos, QuickBird, etc) opens a new era for remote sensing, 
mainly for applications where the need for details is essential as 
in urban environment. With thus images, we can distinguish 
clearly detailed features such as building structure, roads, 
vehicules, and trees (Nakajima et a/., 2002). 
However, the enthusiasm of the users remains measured, 
because of new difficulties in the analysis of these images. 
These difficulties are the need of new techniques to extract 
information from these data and the presence of undesired 
features like shadow of clouds and high objects like buildings 
and trees. Despite a suitable choice of the hour of acquisition, 
shadow is very present on the images of very high spatial 
resolution mainly in urban environment. The amount of shadow 
increase with the spatial resolution. 
The negative effects of shadowing enormously degrade the 
visual quality of the images and cause several nuisances during 
the analysis of these images by modifing spectral response of 
the part of surface in shadow. Thus, surfaces under shadow are 
often confused with other types of objects which have a same 
spectral signatures as the shadow. The results are a 
misclassification of area covered by shadow. All techniques of 
information extraction from image are influenced by the 
presence of shadow by modifing or masking information under 
shadow. For a detailled mapping of urban area, using a very 
high spatial resolution images, it is important to recover 
information under shadow to complete the mapping. But for 
recovering information in shadow, that shadow must be 
accurately detected. 
The object of this study is precisely to well detect shadow in 
images and recover information from surface in shadow and 
  
also to compensate their negative effects by de-shadowing 
images. 
Some techniques for shadow detection are developped in 
moving object detection from videographic images (Prati et a/., 
2001); but, in remote sensing, only few works on shadow 
detection are carried out mainly in builduing detection (Bruce 
Irvin et a/., 1989, Chungan et a/., 1998) and clouds shadow 
correction. Recently, some works on shadow detection on the 
very high spatial resolution like Ikonos are published. Methods 
use principaly color and spectral proprieties to detect shadow 
(Adler-Golden et al., 2002). Some geometrical proprieties (area, 
length, width, parallel sides, right angle, etc) are added to 
improve the detection. Our shadow detection method is based 
also on spectral and form proprieties, but include the sun 
azimuth orientation at the acquistion time. Some contextual 
proprieties (vicinity, relative position sun object and shadow) 
are also added to improve detction or to confirm the results. 
For the restitution of information under shadow, we use the 
contextual and textural features between the shadow detected 
and the neighboor surfaces in the shadow side. These surfaces 
on shadow are supposed to receive projected shadow, even 
surfaces on sun side are supposed to be objects generating 
shadow. The main hypothesis is that the texture features are 
shadow invariant and the neighbooring surface with the same 
texture like shadow is considered to be the same surface under 
that shadow. 
After the surface under shadow is detected, negative effects can 
be removed by transforming shadow pixels value. The 
transforming parameters are calculated for each shadow zone 
using it mean value and the neighboor segment of the same 
surface.