Can DEM Enhance The Digital Image Classification?
DEM Creation
To create the DEM some 35,000 elevation points were digitized along the contour from topo map using PC ARC/INFO. For the plain area, the contour interval digitized were 10 m interval where as it was 20 m interval for the hilly area. This vector based digital terrain elevation data wee rasterized using ERDAS software ERDAS software. After testing several DEM created, a smooth DEM of 30 m pixel size was created using the following algorithm.
e**(-0.5) x (5Q))**2 where, Q = calculated distance/search radius
Image classification
Both unsupervised and
Supervised classification were
Employed to classify the image
With combination of various
original bands, single ratio bands,
NDVI, principal component bands
And DEM to test the classification
Results. A total of 12 combinations
for unsupervised and one for
supervised classification were used
(Table 1). Due to lower spatial
resolution of band 6 of TM data,
except this band, all other bands were
used in the classification process.
Table 1. Band Combinations used.
| Band Combination (BC) |
Bands |
Classification Technique |
1
2
3
4
5
6
7
8
9
10
11
12 |
X2.X3.X4
X1,X4,X5
X2, X4, NDVI
X4,X7/ST, NDVI
X2/X1, X7/X5,NDIV
X2,X4, DEM
X2/X1, X7/X5, DEM
X2, NDVI, DEM
X4, NDVI, DEM
X2/X1,XY/X5,NDVI, DEM
PCI,NDIV, DEM
X7/X5,NDVI,PCI,DEM |
Unsupervised |
| 13 |
NDVI,PC1,DEM |
Supervised |
Note: X = Band number
PC1 = Principal component band 1 |
ISODATA clustering of
Unsupervised approach was used to
Examine the various band combinations. The approach is relatively simple and has considerable intuitive appeal (Vanderzee and Ehrlich, 1995), however, the output of this technique could be affected by the choice of initial parameters and their interactions with each other (LAS, 1990). In this case, the parameters assigned to each band combinations were kept same including number of cluster which was 40. The clusters formed were regrouped with Ward's method which first calculates the means for each variable within each cluster. Then, for each case, it calculates the squared Euclidean distance to the cluster means. These distances are summed for all of the cases. At each step, the two clusters that merge are those that result in the smallest increase in the overall sum of the squared within-cluster distances.
A supervised classification was also run on only one band combination that gave the better result during unsupervised classification. A maximum likelihood classifier with nearest neighbor transformation was used. Results of each band combinations were evaluated creating error matrices and finally, two descriptive statistics, namely producer's and user's accuracy.
Results and Discussion
DEM creation
The interpolated DEM output from the elevation point data wee evaluated randomly selecting 100 sample points and comparing the pixel value with the topo map. The closeness of fit between the extrapolated and topo value wre around 90 percent. A 3-D view of DEM is presented in the form of wire mesh in Figure 1.
Figure 1: A wire-mesh representation of the area
Unsupervised Classification Results
Spectral values for the different land cover classes in different band combinations are presented in Figure 2. For BC1, only harvested agriculture area and mixed deciduous forest could be separated. A mixed class situation was observed between rainfed agriculture, mountainous area and irrigated paddy in the lowland plain were classified as same class due to similar spectral behavior. In BC2, there was no significant improvement in the result. The situation was similar to the previous one.
Figure 2. Spectral values for different land cover classes in different band combinations.
AG= Agriculture, H= harvested, BA=Burnt area, DF=Deciduous forest, DEF=Dry evergreen forest, MDF=Mixed deciduous forest, WB=Waterbody, IP=Irrigated paddy, DDF=Dry dipterocarp forest, OV=Other vegetation, F=Forest, LLV=Lowland vegetation, SCL(P)= Scrubland in the plain, SP(FH)=Sparse forest at foot hills, LO=Lowland, UP=Upland
In BC3, NDVI helped to separate waterbody and burnt paddy field from other land cover types, however there is still mixed class situation between agriculture, forest and other vegetation types BC4 and BS5 gave the similar. In both cases, ratio band could differentiate the waterbody and burnet area. Similarly, harvested agricultural area was also distinct, however other land cover classes were still in mixed class situation.
In BC6, DEM showed the capability of differentiating 3 distinct forest types but other land cover class wee still observed to be in the mixed class. In BC7, ratio bands with DEM helped to differentiate major land cover classes in the area, however, there was mixed class situation between lowland forest and other tree vegetation. For BC8 and BC9, similar results were observed. Besides 3 types of forest, other land cover classes like harvested agricultural area and scrubland in the plain were also separately grouped due to NDVI and DEM bands. BC10 comprising ratio bands, NDVI and DEM gave improved classification results, specifically to differentiate between irrigated paddy and dry evergreen forest which otherwise were found to be classified as the same class in all previous combinations.
In BC11, the inclusion of the first order band of principal component showed superior results to previous combinations by differentiating major land cover classes, however, there was little bit mixed class situation between irrigated paddy and other lowland vegetation. In BC12, the additional ratio band (X7/X5) did not add much towards improving the result compared to that of BC11.
