基于图像与数据双层融合的高光谱图像拼接

针对传统拼接技术对图像信息利用率不足的问题,提出一种基于图像与数据双层融合的高光谱图像拼接技术。对于图像层,采用尺度不变特征变换算法对图像进行特征提取,使用欧氏距离确定特征匹配范围,根据坐标转换关系对特征进行匹配,完成图像层的拼接;对于数据层,首先将数据拆分高、低位数据,然后采用加权和法计算数据的新值并对其进行拼接,最后通过位移运算合并高、低位数据,完成数据层的拼接;最后将图像与数据按照BIL方式进行存储,完成图像与数据的双层融合。在某地域进行高光谱图像拼接实验,实验结果表明图像层和数据层的平均拼接精度分别为0.9214和0.9663,说明该技术具有有效性和准确性。

Hyperspectral Image Mosaicking Based on Double-Layer Fusion of Image and Data

The traditional mosaicking technology exhibits insufficient utilization of the image information.Therefore,a hyperspectral image mosaicking technique based on the double-layer fusion of image and data is proposed.In case of the image layer,the scale-invariant feature transformation algorithm is used to extract the image features and the Euclidean distance is used to determine the feature matching range.Further,the features are matched according to the coordinate conversion relation to complete image layer mosaicking.In case of the data layer,the data is divided into high and low data.Then,the weighted sum method is used to calculate the new value of data and stitch it,and the high and low data are merged via the displacement operation to complete the mosaicking of the data layer.Finally,the image and data are stored in the BIL mode for completing the double-layer fusion of image with data.The hyperspectral image mosaicking experiment is conducted in a certain area.Experimental results demonstrate that the average mosaicking accuracies of the image and data layers are 0.9214 and 0.9663,respectively,indicating the effectiveness and accuracy of the proposed technique.