一种基于区分度和冗余度的航空图像识别框架
An Aerial Image Recognition Framework using Discrimination and Redundancy Quality Measure
摘要：
航空图像分类在遥感和人工智能中发挥着不可或缺的作用。在本文中，我们提出了一个新的航空图像分类框架，专注于组织每个航空图像的局部补丁到多个判别子图。子图既反映了航空影像的几何特性，又反映了航空影像的色彩分布。首先，每个空间图像被分解成一个集合的区域在其颜色强度。由此，区域连通图（RCG），它模拟空间相邻区域之间的连接，编码的空间上下文的航空图像。其次，采用子图挖掘技术，发现从训练的航空影像构造的RCG中的频繁结构。此后，一组细化的结构中选择的频繁的是高歧视性和低冗余。最后，给定一幅新的航拍图像，提取其与细化结构相对应的子RCG。它们被进一步量化为SVM分类的判别向量。实验结果验证了该方法的有效性。此外，可视化的挖掘子图显示，每个航空图像的判别拓扑发现。
英文摘要：
Aerial image categorization plays an indispensable role in remote sensing and artificial intelligence. In this paper, we propose a new aerial image categorization framework, focusing on organizing the local patches of each aerial image into multiple discriminative subgraphs. The subgraphs reflect both the geometric property and the color distribution of an aerial image. First, each aerial image is decomposed into a collection of regions in terms of their color intensities. Thereby region connected graph (RCG), which models the connection between the spatial neighboring regions, is constructed to encode the spatial context of an aerial image. Second, a subgraph mining technique is adopted to discover the frequent structures in the RCGs constructed from the training aerial images. Thereafter, a set of refined structures are selected among the frequent ones toward being highly discriminative and low redundant. Lastly, given a new aerial image, its sub-RCGs corresponding to the refined structures are extracted. They are further quantized into a discriminative vector for SVM classification. Thorough experimental results validate the effectiveness of the proposed method. In addition, the visualized mined subgraphs show that the discriminative topologies of each aerial image are discovered.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
