Application of context invariants in airport region of interest detection for multi-spectral satellite imagery

In literature, many target-specific methods are available for target detection on satellite images. Yet for many targets, intra-class variance is high. This situation results in decreased detection performance after generalization. Airfield is one of the targets with high intra-class variance in satellite images. This variance is caused by different compositions observed in airfields. Hence, approaches which aim at detecting airfields in specific regions and compositions are either unsuccessful or inapplicable to images taken from different regions. Context invariants make it possible to generalize target detection algorithms for varying target compositions and regions. In this study, context invariants are proposed for airfield region-of-interest detection and it is observed that context invariance plays an important role in developing robust and reliable algorithm for varying region, climate and compositions.