Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection
Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection
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DOI：
10.1016/j.isprsjprs.2020.10.010
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发表时间：
2020-12-01
影响因子：
12.7
通讯作者：
Jones, Melissa K. Ward
中科院分区：
文献类型：
--
作者：
Witharana, Chandi;Bhuiyan, Md Abul Ehsan;Jones, Melissa K. Ward
在北极地区测绘中使用大量极高空间分辨率商业图像是一项新的和积极发展的工作。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。空间分辨率需要准确地描述特征形状和纹理模式，如冰楔多边形（IWP），由于不断退化的永久冻土，而光谱分辨率允许捕获的土地使用和土地覆盖类型，迅速转变表面特征。数据融合，即把具有互补特征的PAN和MS图像结合起来的过程，往往是遥感制图工作流程的一个组成部分。融合过程会产生光谱和空间伪影，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））的分类精度。我们采用了一个详细的多维评估，以了解一系列的八个面向应用的数据融合算法的性能时，适用于VHSR图像场景的DLCNN为基础的映射冰楔多边形。我们的研究结果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。融合方法的选择需要被视为与成功应用DLCNN在VHRS图像上所需的训练数据集同等重要，以便能够准确地绘制整个北极地区的冻土融化。
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.