Abstract
Efficient image augmentation for hyperspectral satellite images requires design of multiband processing models that can assist in improving classification performance for different application scenarios. Existing models either work on dynamic band fusions, or use deep learning techniques for identification of application-specific augmentation operations. Moreover, these models use static augmentations, and do not take into consideration image-specific parameters which limits their efficiency levels. To overcome these limitations, this text proposes design of a novel multimodal object-detection based augmentation model for satellite image sets. The proposed model initially applies a customized YOLO (You Only Look Once) based object detection technique on each of the hyperspectral image bands. This is followed by a context-specific classification layer that assists in identification of detected object types. The identified objects are analyzed via a cascaded dual Generative Adversarial Network (cdGAN), which estimates an object-level importance metric, which is used to evaluate its importance probability levels. Based on these probability levels, an Elephant Herding Optimization (EHO) based hyperspectral band-selection model is used, which assists in identification of high priority image bands for classification purposes. Augmentations on these image bands is controlled via a Firefly Optimizer (FFO) which assists in identification of object-level augmentations for efficient classification of satellite images. The augmented image sets are updated via an Incremental Learning (IL) layer that assists in continuous improvement of accuracy levels for different application scenarios. Due to these optimizations, the proposed model is able to improve classification accuracy by 8.5%, precision by 4.3%, recall by 6.5%, while reducing classification delay by 2.9% when compared with existing augmentation-based classification techniques.