Lifting Scheme Based Deep Network Model for Remote Sensing Imagery Classification

Deep Learning has shown great success in many fields, however, transferring this potential to remote sensing imagery interpretation is still a challenging task due to the special data properties, e.g., low Signal-to-Noise Ratio (SNR), high variation, etc. In this work, a lifting scheme based deep model is presented for remote sensing imagery classification. The main idea underlying this scheme is that, an innovative strategy is adopted to decompose the input image into two compact and low-resolution components, and these components are then fed into a standard Convolutional Neural Network (CNN) for classification task. More precisely, (1) one decomposed component is devoted to enhancing the latent patterns and simultaneously attenuating the random variation in the input, and (2) the other component is used to capture the local structural information in the input. The experimental results show that the lifting deep model is computationally efficient and has promising potential, improving the classification accuracy by about 5.7% and obtaining 2.69 x speed-up compared with the counterpart CNN.