A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification

Hyperspectral image (HSI) classification techniques have been intensively studied and a variety of models have been developed. However, these HSI classification models are confined to pocket models and unrealistic ways of datasets partitioning. The former limits the generalization performance of the model and the latter is partitioned leads to inflated model evaluation metrics, which results in plummeting model performance in the real world. Therefore, we propose a universal knowledge embedded contrastive learning framework (KnowCL) for supervised, unsupervised, and semisupervised HSI classification, which largely closes the gap of HSI classification models between pocket models and standard vision backbones. We present a new HSI processing pipeline in conjunction with a range of data transformation and augmentation techniques that provide diverse data representations and realistic data partitioning. The proposed framework based on this pipeline is compatible with all kinds of backbones and can fully exploit labeled and unlabeled samples with expected training time. Furthermore, we design a new loss function, which can adaptively fuse the supervised loss and unsupervised loss, enhancing the learning performance. This proposed new classification paradigm shows great potentials in exploring for HSI classification technology. The code can be accessed at https://github.com/quanweiliu/KnowCL.

Adapting Vision Transformer for Efficient Change Detection

Most change detection models based on vision transformers currently follow a "pretraining then fine-tuning" strategy. This involves initializing the model weights using large scale classification datasets, which can be either natural images or remote sensing images. However, fully tuning such a model requires significant time and resources. In this paper, we propose an efficient tuning approach that involves freezing the parameters of the pretrained image encoder and introducing additional training parameters. Through this approach, we have achieved competitive or even better results while maintaining extremely low resource consumption across six change detection benchmarks. For example, training time on LEVIR-CD, a change detection benchmark, is only half an hour with 9 GB memory usage, which could be very convenient for most researchers. Additionally, the decoupled tuning framework can be extended to any pretrained model for semantic change detection and multi temporal change detection as well. We hope that our proposed approach will serve as a part of foundational model to inspire more unified training approaches on change detection in the future.

Unpaired MRI Super Resolution with Self-Supervised Contrastive Learning

High-resolution (HR) magnetic resonance imaging (MRI) is crucial for enhancing diagnostic accuracy in clinical settings. Nonetheless, the inherent limitation of MRI resolution restricts its widespread applicability. Deep learning-based image super-resolution (SR) methods exhibit promise in improving MRI resolution without additional cost. However, these methods frequently require a substantial number of HR MRI images for training, which can be challenging to acquire. In this paper, we propose an unpaired MRI SR approach that employs self-supervised contrastive learning to enhance SR performance with limited training data. Our approach leverages both authentic HR images and synthetically generated SR images to construct positive and negative sample pairs, thus facilitating the learning of discriminative features. Empirical results presented in this study underscore significant enhancements in the peak signal-to-noise ratio and structural similarity index, even when a paucity of HR images is available. These findings accentuate the potential of our approach in addressing the challenge of limited training data, thereby contributing to the advancement of high-resolution MRI in clinical applications.