Image Classification: No Features, no Clustering

Abstract

In this paper, we consider the problem of satellite image classification, in which feature extraction is a critical step. One of the most prevalent methods is the Bag-of-Words (BoW) feature representation, which attains state-of-the-art performance in many applications. It has five steps: feature detection, local feature extraction, dictionary learning, feature coding, and feature pooling. In this paper, we focus on the second and third step. We propose a simple yet efficient feature extraction method within the BoW framework. It has two main advantages. Firstly, this method does not need any complex local feature extraction; instead, it uses directly the pixel values from small windows as low level features. Secondly, instead of using a time-consuming clustering algorithm for dictionary learning, a random dictionary is built and applied to feature space quantization. An extensive experimental evaluation has been performed and compared with other feature extraction methods. It is demonstrated that our feature extraction method is quite competitive for optical and SAR satellite image classification.