地理空间分析
判别式
班级(哲学)
计算机科学
分类器(UML)
探测器
支持向量机
上下文图像分类
对象类检测
目标检测
集合(抽象数据类型)
人工智能
地理
遥感
计算机视觉
模式识别(心理学)
图像(数学)
人脸检测
电信
面部识别系统
程序设计语言
作者
Gong Cheng,Junwei Han,Peicheng Zhou,Lei Guo
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing
日期:2014-12-01
卷期号:98: 119-132
被引量:575
标识
DOI:10.1016/j.isprsjprs.2014.10.002
摘要
The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.
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