Super-resolution target identification from remotely sensed images using a Hopfield neural network
Super-resolution target identification from remotely sensed images using a Hopfield neural network
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DOI：
10.1109/36.917895
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发表时间：
2001-04
期刊：
影响因子：
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通讯作者：
A. Tatem;H. Lewis;P. Atkinson;M. Nixon
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文献类型：
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作者：
A. Tatem;H. Lewis;P. Atkinson;M. Nixon
模糊分类技术最近被开发用来估计图像像素的类别组成，但是它们的输出没有提供这些类别如何在像素所代表的瞬时视场内空间分布的指示。因此，虽然使用模糊分类提高了土地覆盖目标识别的准确性，但仍然需要开发能够提供更好的土地覆盖空间表示的强大技术。例如，这种技术可以为确定社会或环境政策提供更准确的土地覆盖指标。研究了利用模糊分类确定的像素组成先验信息，利用Hopfield神经网络更可靠地映射类的空间分布。采用一种方法，利用模糊分类的输出约束Hopfield神经网络作为能量最小化工具。网络收敛到一个能量函数的最小值，定义为一个目标和几个约束。因此，提取每个像素内目标类分量的空间分布可表述为约束满足问题，其最优解由能量函数的最小值确定。这个能量最小值代表了每个像素中类组件空间分布的“最佳猜测”图。将该技术应用于合成和模拟的Landsat TM图像，所得地图提供了所研究土地覆盖的准确和改进的表示，在新记录的精细分辨率图像中，Landsat图像的均方根误差（rmse）约为0.09像素。
Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. Such techniques could provide more accurate land cover metrics for determining social or environmental policy, for example. The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function, defined as a goal and several constraints. Extracting the spatial distribution of target class components within each pixel was, therefore, formulated as a constraint satisfaction problem with an optimal solution determined by the minimum of the energy function. This energy minimum represents a "best guess" map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and simulated Landsat TM imagery, and the resultant maps provided an accurate and improved representation of the land covers studied, with root mean square errors (RMSEs) for Landsat imagery of the order of 0.09 pixels in the new fine resolution image recorded.