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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Superresolution Mapping of Remotely Sensed Image Based on Hopfield Neural Network With Anisotropic Spatial Dependence Model
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Superresolution Mapping of Remotely Sensed Image Based on Hopfield Neural Network With Anisotropic Spatial Dependence Model

机译：基于各向异性空间依赖模型的Hopfield神经网络的遥感图像超分辨率映射

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摘要

Superresolution mapping (SRM) based on the Hopfield neural network (HNN) is a technique that produces land cover maps with a finer spatial resolution than the input land cover fraction images. In HNN-based SRM, it is assumed that the spatial dependence of land cover classes is homogeneous. HNN-based SRM uses an isotropic spatial dependence model and gives equal weights to neighboring subpixels in the neighborhood system. However, the spatial dependence directions of different land cover classes are discarded. In this letter, a revised HNN-based SRM with anisotropic spatial dependence model (HNNA) is proposed. The Sobel operator is applied to detect the gradient magnitude and direction of each fraction image at each coarse-resolution pixel. The gradient direction is used to determine the direction of subpixel spatial dependence. The gradient magnitude is used to determine the weights of neighboring subpixels in the neighborhood system. The HNNA was examined on synthetic images with artificial shapes, a synthetic IKONOS image, and a real Landsat multispectral image. Results showed that the HNNA can generate more accurate superresolution maps than a traditional HNN model.
机译：基于Hopfield神经网络（HNN）的超分辨率地图（SRM）是一种技术，可产生比输入土地覆盖率分数图像更精细的空间分辨率的土地覆盖图。在基于HNN的SRM中，假设土地覆盖类别的空间依赖性是均匀的。基于HNN的SRM使用各向同性空间相关性模型，并向邻域系统中的相邻子像素赋予相等的权重。但是，丢弃了不同土地覆盖类别的空间相关性方向。在这封信中，提出了带有各向异性空间依赖模型（HNNA）的基于HNN的修订版SRM。应用Sobel运算符来检测每个粗分辨率像素处每个分数图像的梯度大小和方向。梯度方向用于确定子像素空间依赖性的方向。梯度幅度用于确定邻域系统中相邻子像素的权重。在具有人造形状的合成图像，合成IKONOS图像和真实的Landsat多光谱图像上检查了HNNA。结果表明，与传统的HNN模型相比，HNNA可以生成更准确的超分辨率地图。

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