Remote Sensing Image Classification: No Features, No Clustering

In this paper, we consider the problem of remote sensing image classification, in which feature extraction and feature coding are critical steps. Various feature extraction methods aim at an abstract and discriminative image representation. Most of them are either theoretically too complex or practically infeasible to compute for large datasets. Motivated by this observation, we propose a simple yet efficient feature extraction method within the bag-of-words (BoW) framework. It has two main innovations. First and most interestingly, this method does not need any complex local feature extraction; instead, it uses directly the pixel values from a local window as low level features. Second, in contrast to many unsupervised feature learning methods, a random dictionary is applied to feature space quantization. The advantage of a random dictionary is that it does not need the time-consuming process of dictionary learning yet without a significant loss of classification accuracy. These two novel improvements over state-of-the-art methods significantly reduce the computational time and enable it scalable to a large data volume. 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 and can achieve rather promising performance figures for both optical and SAR satellite images.