Adaptive multi-scale deep neural networks with perceptual loss for panchromatic and multispectral images classification

Due to the redundancy of imaging systems, multispectral and panchromatic images are of higher spatial resolutions and characterized by different attributes, and are often fused together for accurate land-cover mapping. In this work, we propose a novel framework via adaptive multi-scale convolutional neural networks and perceptual loss function for multispectral and panchromatic images classification. In the proposed scheme, adaptive multi-scale convolutional neural networks are designed to capture the scale information of objects adaptively. And perceptual loss function is constructed via non-local spectral and structural similarities to suppress the interference of unbalanced illumination and speckle noises. A corresponding iteration optimization algorithm is presented to solve the proposed perceptual loss function. Experimental results conducted on three datasets indicate that the proposed framework performs better than the state-of-the-art methods.