3. METHODOLOGY 
In SRSU, super resolution reconstruction aims to improve the 
spatial resolution of images, therefore spatial interpolation may 
be an alternative way to achieve this purpose, although a lot of 
detail information in the higher resolution image can not be 
restored. At a first glance, simple interpolation of an image will 
not perform well in downscaling an image. Fortunately in 
SRSU, the super resolution classification map will be converted 
into the original resolution, and this conversion may alleviate 
the impact of downscaling error on the SRSU. Therefore, it is 
interesting to evaluate the role of spatial interpolation instead of 
super resolution reconstruction in SRSU, because this 
alternative technique is particularly simple. 
The spatial interpolation based spectral unmixing (SISU) has 
similar steps as the SRSU, the only difference is replacing the 
super resolution reconstruction with spatial interpolation. The 
SISU has following steps as 
1) Use spatial interpolation to downscale a remotely 
sensed image to higher resolution. 
2)  Classify the obtained higher resolution image. 
3) Convert the classification map into the proportion maps 
of different endmembers in original resolution, and the 
final spectral unmixing result is derived. 
The algorithm is also illustrated in Figure 1. In this study, a 
bilinear interpolation method was employed for spatial 
interpolation, since it is the easiest interpolation method to 
achieve image downscaling. 
/ Remotely 7 
Sensed Imagery 7 
/ 
/ 
  
| Spatial Interpolation 
¥ 
/ Higher Resolution / 
of Imagery / 
  
Y 
Hard Classification 
  
+ 
p Classification Map 
/ 
/ 
* 
; Resolution Coversion 
  
i 
y Abundance Map in / 
/ m : Z 
/ the original resolution — / 
  
Figure 1. The flowchart of spatial interpolation-based spectral 
unmixing 
4. EXPERIMENTS 
4.1 Study area and data 
The study area and data are the same as the initial work of super 
resolution-based spectral unmixing (Li, Tian et al., 2011). The 
study area was located in a lake in Massachusetts, U.S.A. An 
   
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
   
ASTER image patch was selected as the study material for 
spectral unmixing. To make the image comprise enough mixed 
pixel, the ASTER image was resampled to 30 m resolution. 
Finally the ASTER image has 120 X 102 pixels and three 
spectral bands (Figure 2). 
Besides, an aerial photograph with resolution of 0.5 m was used 
to generate the reference data. At first, the aerial photograph 
was visually interpreted to three types, evergreen tree, bare soil / 
deciduous tree and water. Then the high resolution interpreted 
map was converted into 30 m resolution, which was viewed as 
the reference data for the spectral unmixing result, as shown in 
Figure 3. 
  
Figure 2. The ASTER image (Ban 2) for spectral unmixing, at 
42925: 32"N,, 72916" 39 N/ 
4.20 Procedure 
The procedure of the experiments has following steps: 
1) Manually select sample of the three land cover types from 
the ASTER image. 
2) The ASTER image processed for spectral unmixing, using 
the endmember spectrum in step 1. The image 
magnification time varies from 2 to 6, thus there are five 
results for the spectral unmixing. 
3) Linear spectral unmixing method was also used to generate 
spectral unmixing result to compare with the proposed 
algorithm. The LSU was achieved with fully constrained 
least square (FCLS) algorithm (Heinz and Chang, 2001) 
and multiple endmember spectral mixture analysis 
(MESMA) (Roberts, Gardner et al., 1998), which are two 
kinds of widely used spectral unmixing algorithms. 
4) Hard classification was used as another comparative 
algorithm. And a support vector machine was employed as 
the classifier. 
4.3 Results 
Using the proposed spectral unmixing algorithm and three 
comparative algorithms, the ASTER image was finally unmixed 
as Figures 4-6 show. For the proposed algorithm, only the result 
with magnification factor of four is shown. Since MESMA has 
resulted in some negative values, result of MESMA is 
inconvenient to show. 
   
(a) (b) 
Figure 3. The fractional abundance map of reference data (a) 
water, (5) bare soil / deciduous tree and (c) evergreen tree