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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >A Boosting-Based Approach for Remote Sensing Multimodal Image Classification
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A Boosting-Based Approach for Remote Sensing Multimodal Image Classification

机译：基于Boosting的遥感多模态图像分类方法

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Remote Sensing Images (RSI) have been used as a major source of data, particularly with respect to the creation of thematic maps. This process is usually modeled as a supervised learning task where the system needs to learn the patterns of interest provided by the user and assign a class to the rest of the image regions. Thus, it is common to have images obtained from different sensors, which could improve the quality of thematic maps. However, this requires the creation of techniques to properly encode and combine the different properties of the images. So, this paper proposes a boosting-based technique for classification of regions in RSI that manages to encode features extracted from different sources of data, spectral and spatial domains. The approach is evaluated in an urban and a coffee crop recognition scenarios, achieving statistically better results in comparison with the baselines in urban classification and better results at some baselines for the coffee crop recognition.
机译：遥感图像（RSI）已用作主要数据来源，尤其是在创建专题图方面。该过程通常被建模为有监督的学习任务，其中系统需要学习用户提供的感兴趣的模式并将一个类别分配给其余图像区域。因此，通常具有从不同传感器获得的图像，这可以提高专题图的质量。但是，这需要创建适当地编码和组合图像的不同属性的技术。因此，本文提出了一种基于增强的RSI区域分类技术，该技术可对从不同数据源，光谱域和空间域提取的特征进行编码。该方法在城市和咖啡作物识别场景中进行了评估，与城市分类中的基准相比，在统计上取得了更好的结果，在咖啡作物识别的某些基线上获得了更好的结果。

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