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 
    
SPATIAL INTERPOLATION AS A TOOL FOR SPECTRAL UNMIXING OF REMOTELY 
SENSED IMAGES 
Li Xi *, Chen Xiaoling 
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 
Wuhan, China - li rs@163.com 
Commission VII, WG VII/4 
KEY WORDS: Land Cover, Mapping, Analysis, Algorithms, Image, Spectral 
ABSTRACT: 
Super resolution-based spectral unmixing (SRSU) is a recently developed method for spectral unmixing of remotely sensed imagery, 
but it is too complex to implement for common users who are interested in land cover mapping. This study makes use of spatial 
interpolation as an alternative approach to achieve super resolution reconstruction in SRSU. An ASTER image with three spectral 
bands was used as the test data. The algorithm is evaluated using root mean square error (RMSE) compared with linear spectral 
unmixing and hard classification. The result shows that the proposed algorithm has higher unmixing accuracy than those of the other 
comparative algorithms, and it is proved as an efficient and convenient spectral unmixing tool of remotely sensed imagery. 
1. INTRODUCTION 
Classification of remotely sensed images has long been an issue 
in remote sensing community, since it is indispensable for land 
cover mapping which plays an important role in global change 
studies. Conventional technique of image classification, also 
called hard classification, labels each remote sensing pixel with 
a single class, and mixed pixels which comprise two or more 
classes are not considered. Thus, the problem of mixed pixels 
hinders precise land cover mapping when using hard 
classification, since all mixed pixels are classified into single 
classes. 
To overcome the problem of mixed pixels, spectral unmixing 
was proposed to decompose mixed pixels into several classes 
and corresponding abundances, which reflect the land cover 
types more accurately. Typical spectral unmixing algorithms 
can be categorized into two types, linear spectral unmixing 
(Roberts, Gardner et al, 1998; Heinz and Chang, 2001) and 
nonlinear spectral unmixing (Huang and Townshend, 2003; Liu, 
Seto et al., 2004; Lee and Lathrop, 2006). In linear spectral 
unmixing, the spectrum of a mixed pixel was viewed as the 
weighted summation of spectra of different endmembers in the 
pixel, whereas the weight is the proportion of the endmember in 
the pixel (Heinz and Chang, 2001). In nonlinear spectral 
unmixing, the mechanism of the spectral mixture mechanism is 
more nonlinear or unknown, and a variety of algorithms, 
including support vector machine model (Brown, Gunn et al., 
1999), neural network model (Liu, Seto et al., 2004; Lee and 
Lathrop, 2006) and physical model (Kimes and Nelson, 1998), 
were proposed to retrieve the proportions of different 
endmembers. Both existing linear and nonlinear spectral 
unmixing focus on how to utilize the spectral information of 
pixels, and the spatial neighbourhood information is always 
ignored although a small number of algorithms attempt to 
utilize the spatial information (Roessner, Segl et al., 2001). 
Recently, a new model named super resolution based spectral 
unmixing (SRSU) was proposed to utilize the spatial 
information in spectral unmixing (Li, Tian et al., 2011). This 
model is totally different from the conventional models, the 
  
* Corresponding author. 
linear and nonlinear models, since it focuses on spatial 
dimension rather than spectral dimension. SRSU has shown 
good performance in land cover mapping, and is a potential 
powerful approach in the spectral unmixing family. However, 
this model is developed in primary status that an image database 
providing prior knowledge is required, and this perquisite 
makes the model inconvenient for use. This study aims to 
propose a simple way to implement SRSU, and evaluates its 
performance compared with conventional spectral unmixing. 
2. BACKGROUND 
In this section, the previously developed super resolution-based 
spectral unmixing (SRSU) model is introduced. The essence of 
SRSU is that downscaling a remotely sensed image helps to 
reduce spectral mixing in the image and hence the downscaled 
(super resolution) image can be processed with a hard 
classification method to produce the land cover proportion map 
in the original resolution. The SRSU has following steps as 
following steps 1) Use a super resolution technique to 
downscale a remotely sensed image to a super resolution image; 
2) Classify the super resolution image with a hard classification 
method; 3) Convert the super resolution classification map to 
original resolution to produce a set of proportion maps for 
different endmembers, which are the product of spectral 
unmixing. 
The key technique used in SRSU is super resolution 
reconstruction, which refers to reconstruct a higher resolution 
image from the original image. Since this reconstruction is ill- 
posed from a mathematical perspective, prior information 
should be introduced to regulate this process. In super 
resolution reconstruction, prior information is provide by a 
training database, which reflects the relationship of low 
resolution images and their corresponding high resolution 
images in natural scenes. A number of algorithms have been 
proposed to merge the training database into the super 
resolution reconstruction (Freeman et al., 2002; Kim and Kwon, 
2010), but each algorithm is complex.