Discovering relevant spatial filterbanks for VHR image classification
Pattern Recognition(2012)
摘要
In very high resolution (VHR) image classification it is common to use spatial filters to enhance the discrimination among landuses related to similar spectral properties but different spatial characteristics. However, the filters types that can be used are numerous (e.g. textural, morphological, Gabor, wavelets, etc.) and the user must pre-select a family of features, as well as their specific parameters. This results in features spaces that are high dimensional and redundant, thus requiring long and suboptimal feature selection phases. In this paper, we propose to discover the relevant filters as well as their parameters with a sparsity promoting regular-ization and an active set algorithm that iteratively adds to the model the most promising features. This way, we explore the filters/parameters input space efficiently (which is infinitely large for continuous parameters) and construct the optimal filterbank for classification without any other information than the types of filters to be used.
更多查看译文
关键词
channel bank filters,image classification,image matching,image resolution,VHR image classification,features spaces,optimal filterbank,spatial characteristics,spatial filter bank discovery,spectral properties,suboptimal feature selection phases,very high resolution image classification
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络