Semantic Classification of Heterogeneous Urban Scenes Using Intrascene Feature Similarity and Interscene Semantic Dependency

计算机科学 人工智能 特征(语言学) 语义相似性 模式识别(心理学) 相似性(几何) 上下文图像分类 图像(数学) 哲学 语言学
作者
Xiuyuan Zhang,Shihong Du,Yi‐Chen Wang
出处
期刊:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:8 (5): 2005-2014 被引量:48
标识
DOI:10.1109/jstars.2015.2414178
摘要

Semantic classification of urban scenes aims to classify scenes composed of many different types of objects into predefined semantic classes. To learn the association between urban scenes and semantic classes, five tasks are needed: 1) segmenting the image into scenes; 2) establishing semantic classes of scenes; 3) extracting and transforming features; 4) measuring the intrascenes feature similarity; and 5) labeling each scene by a semantic classification method. Despite many efforts on these tasks, most existing works consider only visual features with inconsistent similarity measurement, while ignore semantic features inside scenes and the interactions between scenes, leading to poor classification results for high heterogeneous scenes. To solve these problems, this study combines intrascene feature similarity and interscene semantic dependency to form a two-step classification approach. For the first step, visual and semantic features are first optimized to be invariant to affine transformation, and then are employed in K-Nearest Neighbor to initially classify scenes. For the second step, multinomial distribution is presented to model both the spatial and semantic dependency between scenes, and then used to improve the initial classification results. The implementations conducted in two study areas indicate that the proposed approach produces better results for heterogeneous scenes than visual interpretation, as it can discover and model the hidden information between scenes which is often ignored by existing methods. In addition, compared with the initial classification, the optimized step improves accuracies by 3.6% and 5% in the two study areas, respectively.

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