Interpreting remote sensing images by combining manual visual interpretation and computer automatic classification and recognition is an important application of human–computer interaction (HCI) in the field of remote sensing. Remote sensing images with high spatial resolution and high spectral resolution is an important basis for automatic classification and recognition. However, such images are often difficult to obtain directly. In order to solve the problem, a novel pan-sharpening method via multi-scale and multiple deep neural networks is presented. First, the non-subsampled contourlet transform (NSCT) is employed to decompose the high resolution (HR)/low resolution (LR) panchromatic (PAN) images into the high frequency (HF)/low frequency (LF) images, respectively. For pan-sharpening, the training sets are only sampled from the HF images. Then, the DNN is utilized to learn the feature of the HF images in different directions of HR/LR PAN images, which is trained by the image patch pair sampled from HF images of HR/LR PAN images. Moreover, in the fusion stage, NSCT is also employed to decompose the principal component of initially amplified LR multispectral (MS) image obtained by the transformation of adaptive PCA (A-PCA). The HF image patches of LR MS, as the input data of the trained DNN, go through forward propagation to obtain the output HR MS image. Finally, the output HF sub-band images and the original LF sub-band images of LR MS image fuse into a new sub-band set. The inverse transformations of NSCT and A-PCA , residual compensation are conducted to obtain the pan-sharpened HR MS. The experimental results show that our method is better than other well-known pan-sharpening methods.

通过将人工视觉解释与计算机自动分类识别相结合来解释遥感图像是人机交互（HCI）在遥感领域中的重要应用。具有高空间分辨率和高光谱分辨率的遥感图像是自动分类和识别的重要基础。但是，这样的图像通常很难直接获得。为了解决这一问题，提出了一种基于多尺度和多深度神经网络的泛锐化方法。首先，采用非下采样轮廓波变换（NSCT）将高分辨率（HR）/低分辨率（LR）全色（PAN）图像分解为高频（HF）/低频（LF）图像。对于泛锐化，仅从HF图像中采样训练集。然后，利用DNN学习HR / LR PAN图像在不同方向上的HF图像的特征，通过从HR / LR PAN图像的HF图像中采样的图像补丁对来训练该特征。此外，在融合阶段，NSCT还用于分解通过自适应PCA（A-PCA）转换而获得的最初放大的LR多光谱（MS）图像的主要成分。LR MS的HF图像块作为训练DNN的输入数据，经过正向传播以获得输出HR MS图像。最后，输出的HF子带图像和LR MS图像的原始LF子带图像融合成一个新的子带集。进行了NSCT和A-PCA的逆变换，残差补偿，从而获得了锐利的HR MS。