...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Supervised and Adaptive Feature Weighting for Object-Based Classification on Satellite Images
【24h】

Supervised and Adaptive Feature Weighting for Object-Based Classification on Satellite Images

机译：有监督和自适应特征加权的卫星图像基于对象的分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Object-based image analysis (OBIA) technique has been representing an evolving paradigm of remote sensing application, along with more high-resolution satellite images available. However, too many derived features from segmented objects also present a new challenge to OBIA applications. In this paper, we present a supervised and adaptive method for ranking and weighting features for object-based classification. The core of this method is the feature weight maps for each land type resulted from prior thematic maps and their corresponding satellite images of study areas. Specifically, first, satellite images to be classified are segmented using an adaptive multiscale algorithm, and the multiple (spectral, shape, and texture) features of segmented objects are calculated. Second, we extract distance maps and feature weight vectors for each land type from the prior thematic maps and corresponding satellite images, to generate feature weight maps. Third, a feature-weighted classifier with the feature weight maps, is applied on the segmented objects to generate classification maps. Finally, the classification result is evaluated. This approach is applied on a Sentinel-2 multispectral satellite image and a Google Map image to produce objected-based classification maps, compared with the traditional feature selection algorithms. The experimental results illustrate that the proposed method is practically efficient to select important features and improve classification performance.
机译：基于对象的图像分析（OBIA）技术已经代表了不断发展的遥感应用范例，以及更多可用的高分辨率卫星图像。但是，来自分段对象的太多派生特征也给OBIA应用程序带来了新挑战。在本文中，我们提出了一种基于对象的分类的排序和加权特征的监督和自适应方法。该方法的核心是每个土地类型的特征权重图，这些权重图是由先前的专题图及其研究区域的相应卫星图像生成的。具体而言，首先，使用自适应多尺度算法对要分类的卫星图像进行分割，然后计算分割后的物体的多个（光谱，形状和纹理）特征。其次，我们从先前的专题图和相应的卫星图像中提取每种土地类型的距离图和特征权向量，以生成特征权图。第三，将具有特征权重图的特征加权分类器应用于分割的对象以生成分类图。最后，对分类结果进行评估。与传统特征选择算法相比，该方法应用于Sentinel-2多光谱卫星图像和Google Map图像，以生成基于对象的分类图。实验结果表明，该方法在选择重要特征和提高分类性能方面是有效的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号