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Land use/land cover change is an important field in global environmental change research. Remote sensing is a valuable
data source from which land use/land cover change information can be extracted efficiently. A number of techniques for
accomplishing change detection using satellite imagery have been formulated, applied, and evaluated, which can be
generally grouped into two types. (1) Those based on spectral classification of the input data such as post-classification
comparison and direct two-date classification; and (2) those based on radiometric change between different acquisition
dates. The shortage of type 1 is cumulative error in image classification of an individual date. However, radiometric
change approaches has a strict requirement for reliable image radiometry.
In light of the above mentioned drawbacks of those two types of change detection methods, this paper presents a new
method named change vector analysis in posterior probability space (CVAPS). Change-vector analysis (CVA) is one of
the most successful radiometric change-based approaches. CVAPS approach incorporates post-classification
comparison method and CVA approach, which is expected to inherit the advantages of two traditional methods and
avoid their defects at the same time. CVAPS includes the following four steps. (1) Images in different periods are
classified by certain classifier which can provide posterior probability output. Then, the posterior probability can be
treated as a vector, the dimension of which is equal to the number of classes. (2) A procedure similar with CVA is
employed. Compared with traditional CVA, new method analyzes the change vector in posterior probability space
instead of spectral feature space. (3) A semiautomatic method, named Double-Window Flexible Pace Search (DFPS), is
employed to determine the threshold of change magnitude. (4) Change category is discriminated by cosines of the
change vectors.
CVAPS approach was applied and validated by a case study of land use change detection in urban area of Shenzhen,
China using multi-temporal TM data. Kappa coefficients of "change/no-change" detection and "from-to" types of
change detection were employed for accuracy assessment. The experimental results show that CVAPS outperform than
post-classification comparison method and can avoid cumulative error effectively. Besides, radiometric correction is not
needed in this method compared with traditional CVA. Therefore, it is indicated that CVAPS is potentially useful in
land-use/land-cover change detection.
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