This paper discusses a content based remote sensing image retrieval technique using multi-scale, patch-based local ternary pattern and fisher vector encoding. The technique downsamples an image in three scales, each downsampled image utilizes local ternary pattern to obtain upper and lower texture images, and divides them into dense patches to build a final histogram representation. This representation is then encoded into normalized fisher vectors. To this end, we focus on two standard remote sensing datasets namely 20-class satellite remote sensing dataset and 21-class land-cover dataset. Visual and quantitative results signify high precision for the proposed technique with an improvement of approximately 5.71% and 6.57% for 21-class land-cover and 20-class satellite remote sensing datasets respectively.

本文讨论了一种基于内容的遥感图像检索技术，该技术使用多尺度，基于补丁的局部三元模式和费舍尔矢量编码。该技术以三个比例对图像进行降采样，每个降采样后的图像均使用局部三元模式来获取上下纹理图像，并将其分成密集的小块以构建最终的直方图表示。然后将该表示编码为归一化的费舍尔向量。为此，我们集中于两个标准的遥感数据集，即20类卫星遥感数据集和21类土地覆盖数据集。视觉和定量结果表明该技术具有较高的精度，对于21类土地覆盖和20类卫星遥感数据集分别提高了约5.71％和6.57％。