Department of Software Engineering, Mirpur University of Science & Technology (MUST), Mirpur AJK, Pakistan.
Department of Computer Science, National Textile University, Faisalabad, Pakistan.
PLoS One. 2019 Jul 19;14(7):e0219833. doi: 10.1371/journal.pone.0219833. eCollection 2019.
The classification of high-resolution satellite images is an open research problem for computer vision research community. In last few decades, the Bag of Visual Word (BoVW) model has been used for the classification of satellite images. In BoVW model, an orderless histogram of visual words without any spatial information is used as image signature. The performance of BoVW model suffers due to this orderless nature and addition of spatial clues are reported beneficial for scene and geographical classification of images. Most of the image representations that can compute image spatial information as are not invariant to rotations. A rotation invariant image representation is considered as one of the main requirement for satellite image classification. This paper presents a novel approach that computes the spatial clues for the histograms of BoVW model that is robust to the image rotations. The spatial clues are calculated by computing the histograms of orthogonal vectors. This is achieved by calculating the magnitude of orthogonal vectors between Pairs of Identical Visual Words (PIVW) relative to the geometric center of an image. The comparative analysis is performed with recently proposed research to obtain the best spatial feature representation for the satellite imagery. We evaluated the proposed research for image classification using three standard image benchmarks of remote sensing. The results and comparisons conducted to evaluate this research show that the proposed approach performs better in terms of classification accuracy for a variety of datasets based on satellite images.
高分辨率卫星图像的分类是计算机视觉研究领域的一个开放性研究问题。在过去的几十年中，Bag of Visual Word (BoVW) 模型已被用于卫星图像的分类。在 BoVW 模型中，无序的视觉词直方图（没有任何空间信息）被用作图像签名。由于这种无序性质，BoVW 模型的性能受到影响，并且添加空间线索被报道对图像的场景和地理分类有益。大多数可以计算图像空间信息的图像表示形式都不是旋转不变的。旋转不变的图像表示形式被认为是卫星图像分类的主要要求之一。本文提出了一种新颖的方法，为 BoVW 模型的直方图计算空间线索，该方法对图像旋转具有鲁棒性。通过计算正交向量的直方图来计算空间线索。这是通过计算相对于图像几何中心的一对相同视觉单词（PIVW）之间的正交向量的大小来实现的。通过与最近提出的研究进行比较分析，获得了卫星图像的最佳空间特征表示。我们使用遥感的三个标准图像基准来评估所提出的研究在图像分类方面的性能。为了评估这项研究，我们进行了结果和比较，结果表明，该方法在基于卫星图像的各种数据集的分类准确性方面表现更好。
