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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Superresolution Land Cover Mapping Using Spatial Regularization
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Superresolution Land Cover Mapping Using Spatial Regularization

机译：使用空间正则化的超分辨率土地覆盖图

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摘要

Superresolution mapping (SRM) is a method of predicting the spatial locations of land cover classes within mixed pixels in remotely sensed images. This paper proposes a novel SRM framework that is operated from the perspective of spatial regularization. Within the proposed framework, SRM aims to generate final superresolution land cover maps that conform to inputted fraction images, with spatial regularization intended for exploiting a priori knowledge about the land cover maps. Two SRM models are constructed by using maximal spatial dependence as the spatial regularization term and the L1 or L2 norm as the data fidelity term. The proposed models are evaluated by using synthetic Landsat, real IKONOS, and real Airborne Visible/Infrared Imaging Spectrometer images and compared with hard classification technologies, as well as pixel-swapping, Hopfield neural network, and Markov random field SRM models. We perform linear spectral mixture analysis (LSMA) and multiple endmember spectral mixture analysis (MESMA) to estimate fraction images. Results show that the accuracy of inputted fraction images plays an important role in the final superresolution land cover maps and that using MESMA fraction images results in higher accuracy than using LSMA fraction images. Moreover, the L-curve criterion is suitable for choosing the optimal regularization parameter in both SRM models. Compared with hard classification technologies and other SRM models, the proposed model derives the highest Kappa coefficients and lowest class area proportion errors when MESMA fraction images are used as input.
机译：超分辨率映射（SRM）是一种预测遥感图像中混合像素内土地覆盖类别的空间位置的方法。本文提出了一种新颖的SRM框架，该框架从空间正则化的角度进行操作。在提出的框架内，SRM的目标是生成符合输入分数图像的最终超分辨率土地覆盖图，并进行空间正则化，以利用有关土地覆盖图的先验知识。通过使用最大空间相关性作为空间正则项并使用L1或L2范数作为数据保真度项来构建两个SRM模型。使用合成的Landsat，真实的IKONOS和真实的机载可见/红外成像光谱仪图像对提出的模型进行评估，并与硬分类技术以及像素交换，Hopfield神经网络和Markov随机场SRM模型进行比较。我们执行线性光谱混合分析（LSMA）和多端元光谱混合分析（MESMA）以估计馏分图像。结果表明，输入的分数图像的准确性在最终的超分辨率土地覆盖图中起着重要作用，并且使用MESMA分数图像比使用LSMA分数图像具有更高的准确性。此外，L曲线准则适用于在两个SRM模型中选择最佳正则化参数。与硬分类技术和其他SRM模型相比，当使用MESMA分数图像作为输入时，所提出的模型得出最高的Kappa系数和最低的类面积比例误差。

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