Target detection is an important problem in remote-sensing with crucial applications in law-enforcement, military and security surveillance, search-and-rescue operations, and air traffic control, among others. Owing to the recently increased availability of computational resources, deep-learning based methods have demonstrated state-of- the-art performance in target detection from unimodal aerial imagery. In addition, owing to the availability of remote-sensing data from various imaging modalities, such as RGB, infrared, hyper-spectral, multi-spectral, synthetic aperture radar, and lidar, researchers have focused on leveraging the complementary information offered by these various modalities. Over the past few years, deep-learning methods have demonstrated enhanced performance using multi-modal data. In this work, we propose a method for vehicle detection from multi-modal aerial imagery, by means of a modified YOLOv3 deep neural network that conducts mid-level fusion. To the best of our knowledge, the proposed mid-level fusion architecture is the first of its kind to be used for vehicle detection from multi-modal aerial imagery using a hierarchical object detection network. Our experimental studies corroborate the advantages of the proposed method.
Most commonly used classification algorithms process data in the form of vectors. At the same time, mod- ern datasets often comprise multimodal measurements that are naturally modeled as multi-way arrays, also known as tensors. Processing multi-way data in their tensor form can enable enhanced inference and classification accuracy. Tucker decomposition is a standard method for tensor data processing, which however has demonstrated severe sensitivity to corrupted measurements due to its L2-norm formulation. In this work, we present a selection of classification methods that employ an L1-norm-based, corruption-resistant reformulation of Tucker (L1-Tucker). Our experimental studies on multiple real datasets corroborate the corruption-resistance and classification accuracy afforded by L1-Tucker.