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Model-based incoherent polarimetric decomposition is a frequently used technique to analyze multilook data of polarimetric synthetic aperture radars (POLSARs). The purpose of this study is to analyze and compare different model-based incoherent polarimetric decomposition algorithms from the polarimetric information change aspect. For the input of a model-based incoherent polarimetric decomposition algorithm, polarimetric entropy was used to represent the polarimetric information of a coherency matrix. For the output of a model-based incoherent polarimetric decomposition algorithm, there are usually several decomposed components. To quantitatively represent their total polarimetric information, a new concept, generalized polarimetric entropy, was proposed which generalized the concept of polarimetric entropy based on the information entropy additivity of information theory. Generalized polarimetric entropy consists of two parts named as polarimetric power entropy and polarimetric residual entropy, respectively. Polarimetric power entropy describes the distribution status of the Span values of all decomposed components. Polarimetric residual entropy represents the residual randomness of all decomposed components. With the three new concepts, eight model-based incoherent polarimetric decomposition algorithms were compared and analyzed. Two real POLSAR images, respectively, derived by the E-SAR airborne system of Germany and the GF-3 satellite of China were used for the experiments. Experimental results had illustrated several useful conclusions. [ABSTRACT FROM AUTHOR]