Patch-Based Tiny Object Classification for High Resolution Aerial Images

The multiclass classification of tiny objects of images taken from unmanned aerial vehicles (UAVs) is a challenging task within the field of computer vision research. In order to address this challenge, we propose an efficient patch-based tiny object classification framework. To improve the classification performance and optimize the model efficiency, we present the one-vs-all binary classification-based problem decomposition approach, a z-score-based cluster sampling for selecting the discriminative patches to train the binary classifiers, integration of the decisions from different classifiers at patch level, and finally application of a neighborhood-based image-level decision-fusion to get the image-level label. Using a high-resolution aerial image database, we demonstrate the effectiveness of our approach.