Leu-M

Fast Fourier Transform

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object detection, land use and land cover (LULC) classification, segmentation, and object-based image analysis (OBIA). This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.

Deep learning in remote sensing applications: A meta-analysis and review

FusionGAN: A generative adversarial network for infrared and visible image fusion

Infrared and visible image fusion methods and applications: A survey

The aim of camouflaged object detection (COD) is to find objects that are hidden in their surrounding environment. Due to the factors like low illumination, occlusion, small size and high similarity to the background, COD is recognized to be a very challenging task. In this paper, we propose a general COD framework, termed as MSCAF-Net, focusing on learning multi-scale context-aware features. To achieve this target, we first adopt the improved Pyramid Vision Transformer (PVTv2) model as the backbone to extract global contextual information at multiple scales. An enhanced receptive field (ERF) module is then designed to refine the features at each scale. Further, a cross-scale feature fusion (CSFF) module is introduced to achieve sufficient interaction of multi-scale information, aiming to enrich the scale diversity of extracted features. In addition, inspired the mechanism of the human visual system, a dense interactive decoder (DID) module is devised to output a rough localization map, which is used to modulate the fused features obtained in the CSFF module for more accurate detection. The effectiveness of our MSCAF-Net is validated on four benchmark datasets. The results show that the proposed method significantly outperforms state-of-the-art (SOTA) COD models by a large margin. Besides, we also investigate the potential of our MSCAF-Net on some other vision tasks that are highly related to COD, such as polyp segmentation, COVID-19 lung infection segmentation, transparent object detection and defect detection. Experimental results demonstrate the high versatility of the proposed MSCAF-Net. The source code and results of our method are available at https://github.com/yuliu316316/MSCAF-COD. IEEE

MSCAF-Net: A General Framework for Camouflaged Object Detection via Learning Multi-Scale Context-Aware Features

In deep learning (DL)-based multifocus image fusion, effective multiscale feature learning is a key issue to promote fusion performance. In this article, we propose a novel DL model named multiscale feature interactive network (MSFIN), which can segment the source images into focused and defocused regions accurately by sufficient interaction of multiscale features from layers of different depths in the network for multifocus image fusion. Specifically, based on the popular encoder–decoder framework, two functional modules, namely, multiscale feature fusion (MSFF) and coordinate attention upsample (CAU), are designed for interactive multiscale feature learning. Moreover, the weighted binary cross-entropy (WBCE) loss and the multilevel supervision (MLS) strategy are introduced to train the network more effectively. Qualitative and quantitative comparisons with 19 representative multifocus image fusion methods demonstrate that the proposed method can achieve state-of-the-art performance. The code of our method is available at https://github.com/yuliu316316/MSFIN-Fusion .

Multiscale Feature Interactive Network for Multifocus Image Fusion

Deep neural networks (DNNs) have the powerful ability to automatically extract efficient features, which makes them prominent in electroencephalogram (EEG) based seizure prediction tasks. However, current research in this field cannot take the model uncertainty into account, causing the prediction less credible. To this end, we introduce a novel end-to-end patient-specific seizure prediction framework via model uncertainty learning. Specifically, we propose a reparameterized EEG-based lightweight CNN architecture and a modified Monte Carlo dropout (RepNet-MMCD) strategy to improve the reliability of the DNNs-based model. In RepNet, we obtain multi-scale feature representations by applying depthwise separable convolutions of different kernels. After training, depthwise convolutions with different scales are equivalently converted into a single convolution layer, which can greatly reduce computational budgets without losing model performance. In addition, we propose a modified Monte Carlo (MMCD) strategy, leveraging the samples-based temporal information in EEG signals to simulate the Monte Carlo dropout sampling. Sensitivity, false-positive rate (FPR), and area under curve (AUC) of the proposed RepNet-MMCD achieve 93.1%, 0.033/h, 0.950 and 81.6%, 0.056/h, 0.903 on two public datasets, respectively. We further extend the MMCD strategy to the other baseline methods, which can improve the performance of seizure prediction by a clear margin.

EEG-Based Seizure Prediction via Model Uncertainty Learning

A general sample-weighted framework for epileptic seizure prediction

Corticomuscular coupling analysis based on multiple datasets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding the underlying mechanisms of human motor control systems. In this work, we propose a tridirectional statistical modeling and analysis method to identify the coupling relationships between three types of datasets. Different from conventional approaches where only two datasets are considered and the interest is to interpret one dataset by another in a unidirectional fashion, the goal in this paper is to model three data spaces simultaneously in a tridirectional fashion. To address the intersubject variability concern in real-world medical applications, we further propose a group analysis framework based on the proposed method and apply it to concurrent EEG, EMG and behavior signals collected from 8 normal subjects and 9 patients with Parkinson's disease (PD) performing a dynamic motor task. The results demonstrate highly correlated temporal patterns among the three types of signals and meaningful spatial activation patterns. The proposed approach is a promising technique for performing multi-subject and multi-modal data analysis.

Xun Chen

Papers

MSCAF-Net: A General Framework for Camouflaged Object Detection via Learning Multi-Scale Context-Aware Features

Multiscale Feature Interactive Network for Multifocus Image Fusion

EEG-Based Seizure Prediction via Model Uncertainty Learning

A general sample-weighted framework for epileptic seizure prediction

A tridirectional method for corticomuscular coupling analysis in Parkinson's disease