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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features
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Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features

机译：基于中间特征的高分辨率遥感影像语义分类

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摘要

With the resolution improvement of the remote-sensing images, more details are shown clearly. The challenge that comes along is how to boost the relatively low classification accuracy caused by using pixel-based image classification approaches and low-level visual structure. The low-level features (LLF) may not well describe the image due to the semantic gap between low-level visual features and high-level semantics of images. The bag-of-visual-words (BOV) model which generates mid-level features was proposed to bridge the two levels. However, it generally neglects the context information between local patches. In this paper, an object-oriented semantic classification algorithm that combines BOV with the optimal segmentation scale is presented. In this algorithm, BOV addresses the problem of the representation of mid-level for scenes, while the optimal segmentation scale intends to overcome the defect of conventional BOV in lacking of relationship between image patches and to give more thorough description. The object-based BOV is presented to construct mid-level representations for object description instead of LLF, and histogram intersection kernel (HIK) is introduced in support vector machine (SVM) for classification. The experiments conducted on three datasets testify the superiority of the proposed algorithm.
机译：随着遥感影像分辨率的提高，更清晰地显示了更多细节。随之而来的挑战是如何提高由于使用基于像素的图像分类方法和低级视觉结构而导致的相对较低的分类精度。由于低级视觉特征和图像高级语义之间的语义鸿沟，低级特征（LLF）可能无法很好地描述图像。提出了生成中间级别功能的视觉词袋（BOV）模型来桥接两个级别。但是，它通常忽略本地补丁之间的上下文信息。提出了一种结合BOV和最优分割尺度的面向对象语义分类算法。在该算法中，BOV解决了场景中间层表示的问题，而最佳分割尺度旨在克服传统BOV缺乏图像斑块之间的关系的缺陷，并给出更详尽的描述。提出了基于对象的BOV而不是LLF来构造用于对象描述的中级表示，并且在支持向量机（SVM）中引入了直方图相交核（HIK）进行分类。在三个数据集上进行的实验证明了该算法的优越性。

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