Scene classification for aerial images based on CNN using sparse coding technique

Aerial scene classification purposes to automatically label aerial images with specific semantic categories. However, cataloguing presents a fundamental problem for high-resolution remote-sensing imagery HRRS. Recent developments include several approaches and numerous algorithms address the task. This article proposes a convolutional neural network CNN approach that utilizes sparse coding for scene classification applicable for HRRS unmanned aerial vehicle UAV and satellite imagery. The article has two major sections: the first describes the extraction of dense multiscale features multiple scales from the last convolutional layer of a pre-trained CNN models; the second describes the encoding of extracted features into global image features via sparse coding to achieve scene classification. The authors compared experimental outcomes with existing techniques such as Scale-Invariant Feature Transform and demonstrated that features from pre-trained CNNs generalized well with HRRS datasets and were more expressive than low-and mid-level features, exhibiting an overall 90.3% accuracy rate for scene classification compared to 85.4% achieved by SIFT with sparse coding. Thus, the proposed CNN-based sparse coding approach obtained a robust performance that holds promising potential for future applications in satellite and UAV imaging.