Superresolution Mapping of Remotely Sensed Image Based on Hopfield Neural Network With Anisotropic Spatial Dependence Model
Superresolution Mapping of Remotely Sensed Image Based on Hopfield Neural Network With Anisotropic Spatial Dependence Model
复制标题
基于各向异性空间依赖模型Hopfield神经网络的遥感图像超分辨率制图
DOI：
10.1109/lgrs.2013.2291778
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发表时间：
2014-07
影响因子：
4.8
通讯作者：
Fu, Bitao
中科院分区：
文献类型：
--
作者：
Du, Yun;Ling, Feng;Feng, Qi;Fu, Bitao
基于Hopfield神经网络（HNN）的超分辨率制图（SRM）是一种生成比输入土地覆盖分数图像具有更高空间分辨率的土地覆盖图的技术。在基于HNN的SRM中，假设土地覆盖类的空间依赖性是均匀的。基于HNN的SRM使用各向同性空间依赖模型，并在邻域系统中为相邻子像素赋予相等的权重。然而，不同的土地覆盖类的空间依赖方向被丢弃。在这封信中，一个修正的HNN为基础的SRM与各向异性空间依赖模型（HNNA）。Sobel算子用于检测每个分数图像在每个粗分辨率像素处的梯度大小和方向。梯度方向用于确定子像素空间依赖性的方向。梯度幅值用于确定邻域系统中相邻子像素的权重。HNNA的人工形状的合成图像，合成IKONOS图像，和一个真实的陆地卫星多光谱图像进行了检查。结果表明，HNNA可以产生更准确的超分辨率地图比传统的HNN模型。
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.
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影响因子：
4.8
作者：
M. Q. Nguyen;P. Atkinson;H. Lewis
通讯作者：
M. Q. Nguyen;P. Atkinson;H. Lewis
影响因子：
3.4
作者：
Atkinson, Peter M.
通讯作者：
Atkinson, Peter M.
影响因子：
1.3
作者：
Atkinson, PM
通讯作者：
Atkinson, PM
DOI：
10.1016/j.jag.2012.02.008
发表时间：
2012-08
期刊：
Int. J. Appl. Earth Obs. Geoinformation
影响因子：
--
作者：
F. Ling;Xiaodong Li;Fei Xiao;Shiming Fang;Yun Du
通讯作者：
F. Ling;Xiaodong Li;Fei Xiao;Shiming Fang;Yun Du
影响因子：
4.8
作者：
Jing Jin;Bin Wang-;Liming Zhang
通讯作者：
Jing Jin;Bin Wang-;Liming Zhang