2004 
— 
band 
5. All 
> full 
y, (he 
ately 
ected 
g an 
icular 
ive of 
in the 
crop 
valid) 
round 
ining 
vey in 
d for 
n, the 
n the 
parcel 
es) to 
s hard 
| Pasa. 
)-scale 
zitizing 
()-scale 
tabase; 
satellite 
)S wert 
control 
national 
n using 
roduced 
y, since 
magery. 
pology. 
jatching 
cultural 
mdaries, 
nap and 
system. 
with the 
- further 
indsat 7 
h the 15- 
d bands 
spectral 
nds. The 
ace. It is 
in lower 
nd in the 
alike the 
International Archives of the Photogrammetry, Remote Sensing 
  
popular technique of IHS type enhancement, in which the 
spectral characteristic of each bands are not preserved (Liu, 
1999). The Atlantic technique has been integrated to PCI 
Geomatica and available through the function IMGFUSE (PCT, 
2003). The resulting output images were then used as input to 
subsequent geometric correction process. It is important to note 
that the geometric correction is not required prior to data fusion 
in case of Landsat 7 ETM+ since both data are co-registered at 
the satellite. 
Geometric correction: To integrate Landsat 7 ETM+ images 
with the vector polygon parcel boundaries, it was necessary to 
register both data sets to a common map coordinate system. For 
this purpose, vector to image registration is performed using 
Landsat 7 (L1G) images and vector parcel data as reference 
layer. Georeferencing was carried out using just about 35 
ground control points (GCPs) for each subscene through a 
polynomial transformation and nearest neighbor resampling 
method. For all images, RMSE values were calculated less than 
0.5 pixels (7.5 m) in both X and Y directions. 
Parcel based Analysis 
The principal objective of this study was to develop a parcel- 
based classification methodology in the integration of remote 
sensing and GIS in order to map the summer (August) crops 
within the agricultural land parcels. For this purpose, a number 
of agricultural land cover classes were determined for each of 
the Landsat 7 ETM+ images with the help of reference data 
kept in a GIS database. Prior to collecting the training areas, 
boundary pixels of adjacent agricultural parcels were excluded 
using a binary raster mask. Since, boundary pixels manifest the 
spectral mixture of two or more land cover classes, thus cause 
misclassification. Therefore, only those pixels that fall within 
the agricultural parcel were kept for further analyses. 
For each scene, Bands 3, 2, 1 and 4, 5, 3 were utilized as RGB 
components on separate video display windows. Displaying 
bands 4, 5 and 3 as infrared colour composite is useful for 
monitoring the vegetation development during training process. 
The images were overlaid with vector polygon layer and then 
through querying GIS database, boundary of each land cover 
lype were separately displayed over the images to locate the 
known land cover classes and define training areas. Block 
training was carried out to select blocks of pixels from the 
centre parts of the referenced land parcels. During training 
procedure, heterogeneous land cover classes were avoided since 
they would increase the spectral overlap between the classes. A 
number of samples were collected for each class with respect to 
amount and the size of the parcels as well as their dispersion 
throughout the study area. 
The August training set comprises the following eleven 
information classes: corn (Cor), residue (Res), tomato (Tom), 
Sugar beet (Sbe), clover (Clo), pasture (Pas), pepper (Pep), 
Watermelon (Wme), uncultivated land/bare soil (Unc/Bso), rice 
(Ric) and cauliflower (Cfl). The training samples were refined 
through examining their histograms and scatter plots. Those 
Classes that include two or more different spectral 
Characteristics were subdivided into separate spectral classes. 
Such as the corn subclasses: cor-01, cor-02, cor-03 and cor-04. 
The classification accuracy is generally improved when each 
Subclass represented as separate spectral class (Lillesand & 
Kiefer, 1994). 
and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
After completing the training selection for August image, the 
same agricultural parcels were utilized to collect the training 
samples for July and May images, respectively. However, 
training areas were re-edited to reflect any variation in spectral 
characteristic of a particular class or altering land cover type for 
a particular scene. A final set of statistics were generated for 
seven classes in May and twelve classes in July. 
Optimum band selection: Once the training statistics were 
assembled from each band for each land cover, a decision must 
be made to determine the most suitable bands for discriminating 
the land cover classes in the imagery. For this purpose, the 
Principal Component Analysis (PCA) was performed to obtain 
new channels in the classification process. The first four PCA 
channels of each date having total variance higher than 99% 
were used within the analysis. In addition to PCA channels, 
most effective bands were identified through examining class 
separability based on the divergence of the class signatures. As 
a result, a subset of best four bands was extracted for each 
scene. These were ETM+ Bands 3, 4, 5, 7 for May and July 
scenes, and Bands 2, 3, 4 and 7 for August scene. 
Classification: For each date, maximum likelihood classifier 
was utilized with equal probability assumption for all classes. 
The classification was essentially performed using the bands 1 
to 5 and 7. In addition, the first four PCA channels and the best 
four band combinations were also classified. Each classified 
image was subsequently processed to aggregate the spectral 
sub-classes into associated information classes using a scripting 
language. The resulting classified images were then integrated 
with the vector polygon layer to perform parcel-based analysis. 
Parcel-based analysis was performed by computing the class 
frequencies within each parcel and assigning the parcel the 
class labels based on the highest frequency computed from 
pixel-based classified image. The results of parcel-based 
analysis were stored as attribute within the database. 
Regional masking: Prior to the multi-temporal analysis, we 
have performed a masking based on the knowledge obtained 
from the relations between land cover classes and the 
agricultural parcel boundaries. In our case, the knowledge 
represents the cultivation practice in the study area. That is, the 
cultivation of sugar beet was restricted by the government 
agencies into a group of agricultural parcels. And similarly, 
cauliflower was merely grown within the boundaries of Hotanli 
village. Knowing these facts, the database was populated to 
reflect the relations regarding to restriction zones for 
cultivation. Using the database, regions of cultivation were 
defined as regional masks to include and/or to exclude training 
areas of sugar beet and cauliflower. Then using the regional 
masks, parcel-based classification was performed on both July 
and August images, where both sugar beet and cauliflower were 
cultivated. 
Multitemprol Analysis 
The methodology presented in this part of the paper is based on 
enhancing the discrimination potential of supervised image 
classification through combining maximum likelihood 
classifier, parcel-based analyses, and multi-temporal masking 
technique (Figure 3). The method was implemented using 
Landsat 7 ETM+ images. In addition, Principal Component 
Analysis (PCA) channels and the best four bands were used in 
the analyses. Through analyses of multi-temporal images set, it 
is possible to achieve better discrimination of land cover classes 
1087