The invention discloses a learning
algorithm based on dynamic incremental dictionary update. The learning
algorithm based on dynamic incremental dictionary update comprises the following steps of selecting a pre-training sample set, initializing an initial dictionary, and confirming atom numbers m requiring to be increased; on the basis of an OMP (Orthogonal
Matching Pursuit)
algorithm, using the initial dictionary to carry out sparse representation on input samples, and obtaining an initial
sparse coefficient matrix; calculating a residual error after representation, when the residual error is larger than a predefined threshold, adding m atoms in the initial dictionary, and on the basis of an information entropy, initiating the m atoms; adding the initiated m atoms into the initial dictionary, obtaining a new dictionary matrix, and utilizing the new dictionary matrix to carry out sparse
decomposition on the input samples; on the basis of the input samples subjected to sparse
decomposition, utilizing a K-SVD algorithm to update the incremental atoms, confirming the incremental atom with the minimum error, carrying out
decorrelation on the incremental atoms, and outputting a final dictionary when all the samples are trained. The learning algorithm based on dynamic incremental dictionary update has the beneficial effect that a more effective and more sparse representation can be carried out on a
remote sensing data set with a
large size.