To use target information for space transformation in remote sensing data field, artificial immune network theory is
introduced to multi-spectral remote sensing information mining, based on the knowledge of target spectrum. First, the
target spectrums are fuzzy clustered into several subclasses, to retain different features of target in different subclasses.
Then we develop a novel Regional-memory-pattern Artificial Immune Idiotypic Network (RAIN) model based on
artificial idiotypic network theory, and train RAIN with subclasses samples. And then, the affinities of the target
spectrum and other objects can be calculated according to the immune microscopic dynamics including stimulation and
suppression effect. Finally, principal component analysis (PCA) is performed to affinities to explore more weak and
hidden information. With its application in Baoguto Area, Xinjiang Uyghur Autonomous Region China, choosing
tuffaceous siltstone as target object, the result supports the efficiency of the RAIN-affinity-PCA scheme.