The utility of sheer volumes of very high spatial resolution (VHSR) commercial imagery in mapping the Arctic region is new and actively evolving. Commercial satellite sensors typically record image data in low-resolution multispectral (MS) and high-resolution panchromatic (PAN) mode. Spatial resolution is needed to accurately describe feature shapes and textural patterns, such as ice-wedge polygons (IWPs) that are rapidly transforming surface features due to degrading permafrost, while spectral resolution allows capturing of land-use and land-cover types. Data fusion, the process of combining PAN and MS images with complementary characteristics often serves as an integral component of remote sensing mapping workflows. The fusion process generates spectral and spatial artifacts that may affect the classification accuracies of subsequent automated image analysis algorithms, such as deep learning (DL) convolutional neural nets (CNN). We employed a detailed multidimensional assessment to understand the performances of an array of eight application-oriented data fusion algorithms when applied to VHSR image scenes for DLCNN-based mapping of ice-wedge polygons. Our findings revealed the scene dependency of data fusion algorithms and emphasized the need for careful selection of the proper algorithm. Results suggested that the fusion algorithms that preserve spatial character of original PAN imagery favor the DLCNN model performances. The choice of fusion approach needs to be considered of equal importance to the required training dataset for successful applications using DLCNN on VHRS imagery in order to enable an accurate mapping effort of permafrost thaw across the Arctic region.

极高的空间分辨率（VHSR）商业图像的体积在绘制北极地区的地图中的用途是新的并且正在不断发展。商业卫星传感器通常以低分辨率多光谱（MS）和高分辨率全色（PAN）模式记录图像数据。需要空间分辨率来准确描述特征形状和纹理图案，例如由于永久冻土退化而迅速转变表面特征的冰楔多边形（IWP），而光谱分辨率则可以捕获土地利用和土地覆盖类型。数据融合，将具有互补特性的PAN和MS图像组合在一起的过程通常充当遥感制图工作流程的组成部分。融合过程会产生频谱和空间伪影，这些伪影可能会影响后续自动图像分析算法（例如深度学习（DL）卷积神经网络（CNN））的分类准确性。我们采用了详细的多维评估，以了解八种面向应用的数据融合算法在应用于基于DLCNN的冰楔多边形的VHSR图像场景时的性能。我们的发现揭示了数据融合算法的场景依赖性，并强调需要仔细选择合适的算法。结果表明，保留原始PAN图像空间特征的融合算法有利于DLCNN模型的性能。