Hyperspectral images provide significant spatial and spectral information which are widely used in object detection. Two-stage detectors are commonly employed in hyperspectral object detection, where effective region proposals play a crucial role in accurate object localization. However, during non-maximum suppression (NMS) process, the Intersection over Union (IoU) metric based solely on spatial geometric information is inadequate for discriminating between similar proposals. This results in a substantial number of expected proposals with dissimilar characteristics are eliminated. In this paper, we analyze the spectral information in hyperspectral images to distinguish the characteristics of different proposals. Furthermore, this paper proposes the Spectral IoU (SIoU) by introducing spectral signature differences as a new metric. This improves the ability to differentiate between different object instances and increases the recall rate of bounding boxes with high localization confidence in region proposal stage. Moreover, SIoU can be simply integrated into the hyperspectral objection detection frameworks without introducing additional computational complexity. Extensive experiments on the Semi-Supervised Hyperspectral Object Detection Challenge dataset demonstrate the effectiveness of our method.
To promote the super-resolution (SR) technology in real-world applications, the blind SR, involving kernel estimation and image restoration to super-resolve images with unknown degradation, has become one of the research focuses. Most existing methods either implement the above two tasks step-by-step so that do not well consider the compatibility between them, or repeatedly apply two modules over and over again to emphasize cooperation but limit the adaptive development of each one. Towards the above issues, based on the Deep Alternating Network (DAN), a novel training strategy named switching the iteration is proposed in this paper. In the first stage, an estimation module and a restoration module are optimized alternately to promote compatibility. In the second stage, duplicate the pre-trained modules and place them alternately to form a linear structure to promote adaptive development. Extensive experiments on isotropic Gaussian degradation datasets and irregular blur kernel degradation datasets show that the proposed method can achieve visually pleasing results and state-of-the-art performance in blind SR.
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