Jun Li - IEEE Xplore Author Profile

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Scene classification of remote sensing images plays a vital role in Earth observation applications. Among various challenges, occlusion is a prevalent and critical issue in practical applications, particularly when dealing with large-area occlusions caused by clouds, shadows, and man-made structures. Current methods, whether based on occlusion recovery or occlusion-robust feature extraction, gener...Show More
Recent advances in prompt learning have allowed users to interact with artificial intelligence (AI) tools in multiturn dialog, enabling an interactive understanding of images. However, it is difficult and inefficient to deliver information in complicated remote sensing (RS) scenarios using plain language instructions alone, which would severely hinder deep comprehension of the latent content in im...Show More
Deep learning-based methods have demonstrated promising performance in hyperspectral image (HSI) classification. However, the black-box nature of deep learning poses a significant challenge in designing effective network architectures for HSI classification. To overcome this issue, this article presents a representation model-inspired interpretable deep unfolding network (NSR-Net). First, we formu...Show More
Hyperspectral image (HSI) classification is crucial for remote sensing research, while its high-dimensional features make traditional algorithms difficult to cope with. Despite the breakthroughs in deep learning, the high computational complexity and energy consumption limit its application in resource-limited environments. Spiking neural networks (SNNs), mimicking the brain’s information processi...Show More
Artificial intelligence solutions, especially those based on deep learning, have swept through the realm of remote sensing image understanding over the past decade. Despite remarkable research advancements, there remains a gap between state-of-the-art techniques and application requirements. Neural models trained under conventional paradigms typically demonstrate limited capabilities in rapid gene...Show More
The integration of remote sensing and citizen science offers an unprecedented opportunity for observing the Earth and human activities. Recently, heterogeneous data fusion models have been proposed to align representations and geolocations of remote sensing imagery and social media data, such as optimal transport (OT) and geographic OT (GOT). However, these models generally ignore the differences ...Show More
Hyperspectral image (HSI) classification has garnered substantial attention in remote sensing fields. Recent mamba architectures built upon the selective state-space models (S6) have demonstrated enormous potential in long-range sequence modeling. However, the high dimensionality of hyperspectral data and information redundancy pose challenges to the application of S6 in HSI classification, suffer...Show More
Hyperspectral image (HSI) classification constitutes the fundamental research in remote sensing fields. Convolutional neural networks (CNNs) and Transformers have demonstrated impressive capability in capturing spectral-spatial contextual dependencies. However, these architectures suffer from limited receptive fields and quadratic computational complexity, respectively. Fortunately, recent Mamba a...Show More
Deep learning has been widely used in the field of hyperspectral image (HSI) classification, but existing classification methods generally require a large number of labels. With rarely labeled samples, most deep learning methods have the problem of overfitting. Although few-shot learning has developed in this direction in recent years, many methods are weak in exploring the relationships between s...Show More
Hyperspectral sensors can rapidly acquire high-quality spectral data, very useful for urban monitoring applications. Unfortunately, their spatial detail is not fine enough, and methods to enhance this resolution are required. However, conventional super-resolution (SR) methods for multispectral data do not match the requirements needed to maintain high spectral fidelity. Therefore, this article pr...Show More
Multispectral and hyperspectral image fusion has emerged as a highly effective technique for obtaining images with both high spatial and spectral resolution. This is an ill-posed problem that poses significant challenges to the optimization solution, which is often mitigated by incorporating low-rank and smooth priors to restrict the solution space. Traditionally, these priors are combined additiv...Show More
Normalized Difference Vegetation Index (NDVI) data from optical satellites have been widely used in the field of remote sensing. However, due to cloud cover and other extreme weather reasons, NDVI time series are often missing. In order to fill the observation gap under severe weather conditions, an NDVI time series reconstruction method that fuses optical and synthetic aperture radar (SAR) images...Show More
Graph-based methods have excellent performance in hyperspectral image (HSI) classification because of their strong ability to explore the relationship between labeled and unlabeled samples. However, most graph-based methods do not take sufficient account of label information and more discriminant features to establish graph connections, which will lead to a lot of improper connections, and then pr...Show More
Graph theory-based techniques have recently been adopted for anomaly detection in hyperspectral images (HSIs). However, these methods rely excessively on the relational structure within the constructed graphs and tend to downplay the importance of spectral features in the original HSI. To address this issue, we introduce graph frequency analysis to hyperspectral anomaly detection (HAD), which can ...Show More
Hyperspectral image (HSI) classification constitutes a significant foundation for remote sensing analysis. Transformer architecture establishes long-range dependencies with a self-attention mechanism (SA), which exhibits advantages in HSI classification. However, most existing transformer-based methods are inadequate in exploring the multiscale properties of hybrid spatial and spectral information...Show More
The effective reasoning of integrated multimodal perception information is crucial for achieving enhanced end-to-end autonomous driving performance. In this paper, we introduce a novel multitask imitation learning framework for end-to-end autonomous driving that leverages a dual attention transformer (DualAT) to enhance the multimodal fusion and waypoint prediction processes. A self-attention mech...Show More
Supervised hyperspectral image classification suffers from the overfitting problem when limited labels are available. Graph-based semisupervised classifiers can tackle this problem by building connections between labeled and unlabeled samples. In this work, we prove the following two propositions for an optimal graph: 1) the interclass connection weights must be 0 and 2) for a given class, a subse...Show More
In the context of frequent global flood disasters, flood detection is of great significance for emergency management and human sustainable development, especially in urban areas with increasing population and socio-economic activities. However, there are similar reflection/scattering characteristics between flooded and nonflooded land use and land cover (LULC) classes in complex urban environments...Show More
Multilabel remote sensing (RS) image classification aims to predict multiple semantic labels from an RS image. Previous methods [e.g., graph convolution networks (GCNs)] focus on mining the relationships of multiple labels, neglecting that the scene information is closely related to labels. To remedy this deficiency, in this article we propose a novel end-to-end deep neural network for multilabel ...Show More
In recent years, remote sensing recognition technology has found extensive applications in diverse fields, such as modern agriculture, forest management, urban planning, natural resource management, and disaster monitoring. However, the existing remote sensing recognition tasks face significant challenges because of the complex and ever-changing observation environment and the rapid growth of obse...Show More
Multimodal remote sensing image recognition aims to identify a category of land cover for every pixel with consistency and complementary information provided by different modalities. Most existing methods perform land cover recognition in a supervised manner with explicit label guidance. It is challenging to perform recognition without label guidance due to the complex spatial distribution and mod...Show More
Spatiotemporal fusion is an important means to reconstruct the medium spatial resolution remote sensing image series. Presently, many spatiotemporal fusion approaches have been developed and adopted in research on agriculture, ecology, environment, and so on. Although these approaches have achieved remarkable performance in experiments and applications, most of them are designed to fuse all involv...Show More
Hyperspectral image (HSI) super-resolution, which aims at improving the spatial quality of HSIs by fusing a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI), has drawn significant attention. Numerous LR-HSI and HR-MSI (HSI–MSI) fusion algorithms have emerged in recent times, yet they suffer from a lack of generality and integration, which hampers thei...Show More
Standard hyperspectral (HS) pansharpening utilizes panchromatic (PAN) images to improve the connected low (spatial) resolution HS (LRHS) images to the spatial resolutions of PANs, while arbitrary-resolution HS (ARHS) pansharpening aims to use PANs to enhance LRHS images to any desired spatial resolutions. For the challenging task of ARHS pansharpening, one of the major obstacles is how to generali...Show More
Feature extraction is a prevalent technique in hyperspectral remote sensing. Various tasks require this technique as a preprocessing step, including image classification, anomaly detection, image denoising, and so on. Edge-preserving filtering-based methods have been extensively utilized for this purpose. However, these methods do not take the inherent structural and textural information into acco...Show More