Fusing Panchromatic and Swir Bands Based on CNN – A Preliminary Study Over Worldview-3 Datasets

Abstract

ABSTRACT:The traditional fusion methods are based on the fact that the spectral ranges of the Panchromatic (PAN) and multispectral bands (MS) are almost overlapping. In this paper, we propose a new pan-sharpening method for the fusion of PAN and SWIR (short-wave infrared) bands, whose spectral coverages are not overlapping. This problem is addressed with a convolutional neural network (CNN), which is trained by WorldView-3 dataset. CNN can learn the complex relationship among bands, and thus alleviate spectral distortion. Consequently, in our network, we use the simple three-layer basic architecture with 16×16 kernels to conduct the experiment. Every layer use different receptive field. The first two layers compute 512 feature maps by using the 16×16 and 1×1 receptive field respectively and the third layer with a 8×8 receptive field. The fusion results are optimized by continuous training. As for assessment, four evaluation indexes including Entropy, CC, SAM and UIQI are selected built on subjective visual effect and quantitative evaluation. The preliminary experimental results demonstrate that the fusion algorithms can effectively enhance the spatial information. Unfortunately, the fusion image has spectral distortion, it cannot maintain the spectral information of the SWIR image.

An investigation in satellite images based on image enhancement techniques

“…This algorithm provides a significant increase of the efficiency and reliability while updating maps of large cities which reduces the financial cost and also the human errors are minimised. Guo, Ma, Bao, and Wang (2018) proposed an algorithm for fusing panchromatic and short wave infrared bands based on convolutional neural network. This method effectively enhances the spatial information by separating the basic architecture into three layers.…”