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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
POST-CLASSIFICATION APPROACH BASED ON GEOSTATISTICS TO REMOTE
SENSING IMAGES : SPECTRAL AND SPATIAL INFORMATION FUSION
N. Yao**, J. X. Zhang*, Z. J. Lin®, C. F. Ren?
* Remote Sensing information Engineering School, Wuhan University, 129 Luoyu Road, Wuhan, China
nayao@foxmail.com; jxzhang@whu.edu.cn; ren_cf@163.com
® Chinese Academy of Surveying and Mapping, 16 Beitaiping Road, Beijing, China - lincasm@casm.ac.cn
KEY WORDS: Classification, Information, Fusion, Spectral, Spatial, Geostatistics, Kriging
ABSTRACT:
Classification of remote sensing imagery provides an inexpensive yet efficient approach to land cover mapping. In supervised image
classification, training samples are collected through certain sampling schemes, which are used to derive classification rules, aiming
for adequate accuracy for the applications at hand. However, in conventional classification methods, the potential of training
samples in terms of locational information is not tapped further, confounding the classification accuracy to the limited separability
inherent to the given input feature vector. This paper explores two methods pertaining to geostatistics, i.e., simple kriging with local
mean and cokriging, to predict class occurrences based on training samples’ indicator transforms (location and classes) and
spectrally derived class probabilities, thus calibrating the a posterior class probability vectors derived from initial spectral
classification. The results showed that classification accuracy is significantly increased by these two methods for utilizing spatial
information contained in training samples and initial spectral classification, compared with those obtainable with spectral
classification. Moreover, the proposed methods constitute a valuable strategy for making fuller use of information residing in
training data for improving spectrally derived classification, which is independent of the specific classifiers initially adopted for
image classification.
1. INTRODUCTION
Land cover, as a spatial factor impacting and linking human life
and natural environment, refers to the observed (bio)physical
cover on the earth's surface (FAO, 2000). Remote sensing is an
attractive data source for land cover mapping. Although remote
sensing has been used successfully in mapping a range of land
covers at a variety of spatial and temporal scales, the land cover
maps derived are often judged to be of insufficient quality for
operational applications (Foody, 2002). Therefore, how to
improve the quality of land cover maps has been a hot issue all
the time.
Thematic mapping, exemplified by land cover mapping, from
remotely sensed data is typically based on an image
classification (Foody, 2002). Hence, the performance of a
certain classifier would become a key factor which impacts on
the classification accuracy and further impacts on the quality of
the derived maps. An operating classifier can be considered as a
system that reduces the initial uncertainty by consuming the
information contained in the input vector (Battiti, 1995). Battiti
further indicates that the final uncertainty will be zero in the
ideal case (i.e., the class will be certain), while it can be higher
in the actual applications for at least two different reasons, i.e.,
insufficient input information or suboptimal operation.
Hence, the performance of a classifier is one possibility that
relates to suboptimal operation. That is, even if sufficient input
information is given, classification accuracy quantified by the
confusion matrix may be lower than its potential value due to
the insufficient training of the classifier. On the other hand, the
information loss during the training period manifests a
conceivable promotion space of classification accuracy.
* Corresponding author.
Therefore, if a strategy is capable of compensating or reducing
the information loss, an accuracy promotion of classification
would be foreseeable.
Traditional spectral classification of remotely sensed images
applied on a pixel-by-pixel basis ignores the potentially useful
spatial information between the values of proximate pixels
(Atkinson, 2000; Zhang, 2009). Geostatistical approaches,
adopted in this paper, indeed aim to employ the spatial
information inherent in remotely sensed images to enhance the
spectral classification. Spatial information mainly serves two
purposes that derive texture “wavebands” for subsequent use in
classification or smooth the imagery prior to or after
classification (Atkinson, 2000).
This paper utilizes two kriging approaches, i.e., simple kriging
with local and cokriging, to fusion the input (e.g., spectral ) and
spatial information. In the following sections, first the
principles of the two kriging paradigms are revisited, then an
index which assesses the potential separability of a data set is
introduced, and finally the experimental results are presented
and analyzed.
2. METHOD
Spectral response in each waveband of a remotely sensed image
may be treated as a continuous variable, which is also defined
as a regional variable in geostatistics. The variogram (or
covariance function) is a quantitative model which reflects the
relationship and spatial structure of the regional variables.