In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
of multi spectral, hyperspectral and SAR data. Nevertheless, due 
to space constraints, we will focus the attention solely on a 
representative change-detection problem referring to an intense 
farming area experiencing several land-cover transitions located 
in Barrax, a village close to Albacete (Castilla-La Mancha, 
Spain). In particular, available data consist in: two Landsat-5 
Thematic Mapper (TM) images acquired on 15 th July 2003 and 
17 th July 2004, respectively (as generally done in the literature 
among the 7 spectral bands we did not consider the low- 
resolution band associated with the thermal infrared channel); 
one PROBA CHRIS image (composed by 63 spectral bands 
with centre wavelengths from 400 to 1050 nm) acquired on 16 th 
July 2004; and one Envisat ASAR alternate polarization (VV, 
HH) image (despeckled using a 3x3 Gamma filter) acquired on 
18 th July 2004. All of them have been properly co-registered to 
a common spatial geometry of 30 meters and a study area of 
512 x 512 pixels has been selected. July 2003 will be referred 
to as t\, whereas July 2004 will be referred to as h (no signifi 
cant changes indeed occurred between 16 th and 18 th July 2004). 
From the original available images we derived the following 
three datasets (using the stacked vector approach for multi 
source data fusion (Richards and Jia, 2006)) composed by: i) 
the 6 Landsat TM bands at both t\ and h (i.e., A=A=6) 
[Dataset I]; ii) the 6 Landsat TM bands at t\ and the merger of 
the 2 Envisat ASAR backscattering intensity images with the 6 
Landsat TM bands at h (i.e., A =6 and A =8) [Dataset II]; 
and iii) the 6 Landsat TM bands at t\ and the 63 PROBA 
CHRIS bands at h (i.e., A =6 and A =63) [Dataset III], 
However, it is worth noting that our objective is not to seek for 
a set of features at both dates which could be more effective for 
solving the investigated problem, but rather to demonstrate that 
the presented method is even capable of effectively handling 
different types of data at the two times. 
As described in Section 3, the user is required to set the number 
of Gaussian kernels K to be employed for approximating the 
PDFs. Hence, in order to understand how significant the selec 
tion of this free parameter is, we performed a series of experi 
ments varying K from 20 to 120 with steps of 10. 
According with a variety of experiments on toy and real data 
sets we fixed f = 10" 4 and /3=10 2 . 
In all the trials we employed the ¿-means clustering for initializ 
ing both centres and variances of kernel functions. Neverthe 
less, the very high complexity of the algorithm (i.e., approxi 
mately 0(iV z>(X+1) logA r ), where N and D represent the num 
ber of samples to be clustered and their dimensionality, respec 
tively (Inaba et al., 1994)), prevented us from using all the pat 
terns of each investigated image at a time, as this would have 
required a very high computational burden. In order to over 
come this limitation, for each image we ran the ¿-means algo 
rithm on a random subset containing one third of the total 
amount of samples. However, as this might affect the final 
change-detection accuracies of the proposed technique, for each 
number of considered kernels K , we performed 10 different 
trials running each time the ¿-means clustering on a different 
random subset. Moreover, we finally also combined the 10 
resulting change-detection maps through a majority voting en 
semble. 
For validating the potentialities of the presented method, we 
compared the results with those obtained by supervised PCC. In 
particular, we considered ML and SVM fully-supervised classi 
fiers trained by exploiting a complete ground truth for all the 
land-cover classes characterizing each considered date. ML is a 
simple yet generally rather effective statistical classifier, which 
does not require the user to set any free parameter (Richards 
and Jia, 2006). SVM are advanced state-of-art classifiers, which 
proved capable of outperforming other traditional approaches 
(Cristianini and Shawe-Taylor, 2000). For the selection of the 
two free parameters (i.e., a penalization parameter and the vari 
ance of considered Gaussian kernels) we employed a 10-fold 
cross-validation strategy (Duda et al., 2000). 
Available prior knowledge has been used for defining regions 
of interest composed on the whole by 21941 pixels whose 
ground truth was known at the two times, respectively. 10 land- 
cover classes have been considered at t\, whereas 9 have been 
taken into consideration at ti. At both dates, from all the avail 
able labeled samples we defined two spatially-disjoint training 
sets (see Table 1). This means that there is no overlapping be 
tween training samples at U and h . All of them have been used 
for training both the ML and SVM supervised classifiers at each 
time. 
Land-cover class 
t\ (July 2003) 
h (July 2004) 
alfalfa 
2031 
634 
bare soil 
2585 
- 
com 
1737 
2664 
garlic 
101 
302 
grasslands 
- 
42 
onions 
213 
220 
poppy 
336 
- 
potatoes 
208 
283 
spring crops 
- 
3318 
stubble 
2416 
2247 
sugar beet 
365 
- 
sunflower 
- 
449 
wheat 
369 
total 
10361 
10159 
Table 1. Number of spatially-disjoint training samples 
considered at the two dates. 
Change-detection results have been evaluated (over those sam 
ples whose land-cover class is known at both dates) in terms of: 
percentage overall accuracy OA% (i.e., the percentage of sam 
ples correctly identified as both changed or unchanged over the 
whole number of samples), and kappa coefficient of accuracy 
(which also takes into consideration errors and their type) 
(Richards and Jia, 2006). 
Among the different land-cover transitions occurred between 
the two dates, here we take into consideration (one at a time) 
two of them, namely “bare soil to spring crops” and “alfalfa to 
com” (experienced by 4583 and 1035 pixels, respectively, over 
the whole available 21941 whose ground truth was known at 
both times). 
In our trials, we empirically experienced that a common range 
for K resulting in average high detection accuracies spans 
from 60 to 80. When instead nearing the lower or the upper 
bound of the considered interval, performances tend to vary 
depending on the specific land-cover transition of interest. Ac 
cordingly, in Table 2 and Table 3 we show the results obtained 
with the proposed technique for K = 40, 60, 80, 100. In par 
ticular, we report the median over the 10 realizations with dif 
ferent ¿-means clustering initialization. Moreover, also accura 
cies finally obtained with the majority voting ensemble (de 
noted as PSCD^), as well as those obtained by supervised PCC 
using ML and SVM (denoted as PCC^ and PCC 5[ / M , respec 
tively) are presented. 
While investigating the “bare soil to spring crops” transition 
with the proposed PSCD technique, from all the training pixels 
reported in Table 1, we considered the only 2585 available for 
bare soil at t\ and the only 3318 spatially-disjoint available for