A remote sensing image classification method for multi-task iterative learning and memorizing is provided and includes: initializing previous parameters, a fixed weight, and a temporary weight, performing a prediction performance; calculating a sensitivity of each parameter; training the model by using a modified loss function to thereby learn the previous parameters, when the model learns a new task; developing a new neuron to learn a new class in the new task, re-initializing the temporary weight to learn the temporary weight, performing the prediction performance; calculating the sensitivity matrix of parameters added in a loss function for a training of a next task by using the sensitivity matrix of the parameters by using training data of the new task; repeating the above steps when the new task is coming for training; performing a remote sensing image classification by using the classifier of the model as trained. start initializing previous parameters of a classifier of a model, a fixed weight of the classifier, and a temporary weight of the classifier, obtaining the and the by training, assigning the to the , and performing a prediction performance by using the and the calculating sensitivity matrix of parameters modifying an original loss function and adding a regularization term, when the model learns a new task, and learning the previous parameters of the classifier developing a new neuron, re-initializing the temporary weight of the classifier, training the model to obtain the , assigning the to the , and performing the prediction performance by using the and the calculating the sensitivity matrix of the parameters of a current task by the model; accumulating and averaging the sensitivity matrix of the parameters of the current task and the sensitivity matrix as previously calculated to obtain the sensitivity matrixes of the parameters as new a new task is coming end
REMOTE SENSING IMAGE CLASSIFICATION METHOD FOR MULTI-TASK ITERATIVE LEARNING AND MEMORIZING