Using deep learning to classify hyperspectral image(HSI) with only a few labeled samples available is a challenge. Recently, the knowledge distillation method based on soft label generation has been used to solve classification problems with a limited number of samples. Unlike normal labels, soft labels are considered the probability of a sample belonging to a certain category, and are therefore more informative for the sake of classification. The existing soft label generation methods for HSI classification cannot fully exploit the information of existing unlabeled samples. To solve this problem, we propose a novel self-supervised learning method with knowledge distillation for HSI classification, termed SSKD. The main motivation is to exploit more valuable information for classification by adaptively generating soft labels for unlabeled samples. First, similarity discrimination is performed using all unlabeled and labeled samples by considering both spatial distance and spectral distance. Then, an adaptive nearest neighbor matching strategy is performed for the generated data. Finally, probabilistic judgment for the category is performed to generate soft labels. Compared to the state-of-the-art method, our method improves the classification accuracy by 4.88%, 7.09% and 4.96% on three publicly available datasets, respectively.

The integration of multi-modality images significantly enhances the clarity of critical details for object detection. Valuable semantic data from object detection enriches the fusion process of these images. However, the potential reciprocal relationship that could enhance their mutual performance remains largely unexplored and underutilized, despite some semantic-driven fusion methodologies catering to specific application needs. To address these limitations, this study proposes a mutually reinforcing, dual-task-driven fusion architecture. Specifically, our design integrates a feature-adaptive interlinking module into both image fusion and object detection components, effectively managing the inherent feature discrepancies. The core idea is to channel distinct features from both tasks into a unified feature space after feature transformation. We then design a feature-adaptive selection module to generate features rich in target semantic information and compatible with the fusion network. Finally, effective combination and mutual enhancement of the two tasks are achieved through an alternating training process. A diverse range of swift evaluations is performed across various datasets to corroborate the potential efficiency of our framework, actualizing visible advancements in both fusion effectiveness and detection accuracy.

In the field of image fusion, the fusion of infrared and visible images emerges as a crucial work, retaining the inherent characteristics of each source image for the creation of high-quality fusion images. However, existing fusion algorithms primarily prioritize visual performance and statistical metrics, often neglecting the requisites of high-level vision tasks. To bridge this gap, our paper proposes SIGFusion, an end-to-end method for infrared and visible image fusion. SIGFusion not only considers the visual performance and statistical metrics of the fused image but also focuses on meeting the demands of subsequent high-level vision tasks, thereby enhancing performance in fusion and high-level vision tasks. Concretely, we define significant semantic information as salient targets presenting in both infrared and visible images. By utilizing targets, our fusion network is guided to enhance these salient targets. In contrast to the isolated employ of either infrared or visible images, our target-based method comprehensively utilizes the semantic information within the image. Additionally, the utilization of target-based self-attention loss enhances the extraction of vital information from the source image. It is worth noting that masks are only necessary during the training stage and are automatically generated through detection algorithms. We conduct experiments in both image fusion and high-level vision tasks. Experimental results demonstrate the effectiveness of SIGFusion in both infrared and visible image fusion tasks, as well as in subsequent high-level vision tasks. The source code is available at https://github.com/poison-pig/SIGFusion.