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Publications (32)
Fine-grained crop classification is crucial for precision agriculture and food security monitoring. It requires simultaneous capture of both phenological dynamics (obtained from multi-temporal satellite data like Sentinel-2) and subtle spectral variations (demanding nanometer-scale spectral resolution from hyperspectral imagery). Research combining...
Accurate crop mapping fundamentally relies on modeling multi-scale spatiotemporal patterns, where spatial scales range from individual field textures to landscape-level context, and temporal scales capture both short-term phenological transitions and full growing-season dynamics. Transformer-based remote sensing foundation models (RSFMs) offer prom...
The proliferation of remote sensing satellites has resulted in a massive amount of remote sensing images. However, due to human and material resource constraints, the vast majority of remote sensing images remain unlabeled. As a result, it cannot be applied to currently available deep learning methods. To fully utilize the remaining unlabeled image...
Cloud detection, as an important preprocessing operation for remote sensing (RS) image analysis, has received increasing attention in recent years. Most of the previous cloud detection methods consider the detection as a pixel-wise image classification problem (cloud versus background), which inevitably leads to a category-ambiguity when dealing wi...
The advancement of remote sensing technology has improved the spatial resolution of satellite imagery, facilitating more detailed visual representations for diverse interpretations. However, existing methods exhibit limited generalization capabilities across varied applications. While some contemporary foundation models demonstrate potential, they...
Obtaining accurate weather forecasts at station locations is a critical challenge due to systematic biases arising from the mismatch between multi-scale, continuous atmospheric characteristic and their discrete, gridded representations. Previous works have primarily focused on modeling gridded meteorological data, inherently neglecting the off-grid...
Obtaining accurate weather forecasts at station locations is a critical challenge due to systematic biases arising from the mismatch between multi-scale, continuous atmospheric characteristic and their discrete, gridded representations. Previous works have primarily focused on modeling gridded meteorological data, inherently neglecting the off-grid...
In an era of frequent extreme weather and global warming, obtaining precise, fine-grained near-surface weather forecasts is increasingly essential for human activities. Downscaling (DS), a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions from global-scale forecast res...
Leveraging the extensive training data from SA-1B, the Segment Anything Model (SAM) demonstrates remarkable generalization and zero-shot capabilities. However, as a category-agnostic instance segmentation method, SAM heavily relies on prior manual guidance, including points, boxes, and coarse-grained masks. Furthermore, its performance in remote se...
Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation. The recent advancements of Convolutional Neural Networks (CNNs) and Transformers have markedly enhanced classification accuracy. Nonetheless, remote sensing scene classification remains a signif...
Accurately retrieving surface meteorological states at arbitrary locations is of great application significance in weather forecasting and climate modeling. Since meteorological variables are typically provided as coarse-resolution gridded fields, common methods obtain the states at a specific location directly through spatial interpolation can lea...
Cloud and snow detection in remote sensing images has advanced significantly with the aid of deep learning methods. However, deep learning methods necessitate a large quantity of labeled data, which consumes a substantial amount of human and material resources. Numerous studies have focused on weakly supervised methods to reduce the workload of ann...
In an era of frequent extreme weather and global warming, obtaining precise, fine-grained near-surface weather forecasts is increasingly essential for human activities. Downscaling (DS), a crucial task in meteorological forecasting and remote sensing, enables the reconstruction of high-resolution meteorological states for target regions from global...
Leveraging vast training data (SA-1B), the foundation Segment Anything Model (SAM) proposed by Meta AI Research exhibits remarkable generalization and zero-shot capabilities. Nonetheless, as a category-agnostic instance segmentation method, SAM heavily depends on prior manual guidance involving points, boxes, and coarse-grained masks. Additionally,...
Despite its fruitful applications in remote sensing, image super-resolution is troublesome to train and deploy as it handles different resolution magnifications with separate models. Accordingly, we propose a highly-applicable super-resolution framework called FunSR, which settles different magnifications with a unified model by exploiting context...
Despite its fruitful applications in remote sensing, image super-resolution (SR) is troublesome to train and deploy as it handles different resolution magnifications with separate models. Accordingly, we propose a highly applicable SR framework called FunSR, which settles different magnifications with a unified model by exploiting context interacti...
Training deep learning-based change detection (CD) model heavily depends on labeled data. Contemporary transfer learning-based methods to alleviate the CD label insufficiency mainly upon ImageNet pre-training. A recent trend is using remote sensing (RS) data to obtain in-domain representations via supervised or self-supervised learning (SSL). Here,...
High-resolution hyperspectral remote sensing images are of great significance to agricultural, urban, and military applications. However, collecting and labeling hyperspectral images are time-consuming, expensive, and usually heavily rely on domain knowledge. In this article, we propose a new method for generating high-resolution hyperspectral imag...
Fine-grained image classification can be considered as a discriminative learning process where images of different subclasses are separated from each other while the same subclass images are clustered. Most existing methods perform synchronous discriminative learning in their approaches. Although achieving promising results in fine-grained visual c...
Global land cover (GLC) products can be utilized to provide geographical supervision for remote sensing representation learning, which has significantly improved downstream tasks’ performance and decreased the demand of manual annotations. However, the time differences between remote sensing images and GLC products may introduce deviations in geogr...
Supervised deep learning models depend on massive labeled data. Unfortunately, it is time-consuming and labor-intensive to collect and annotate bitemporal samples containing desired changes. Transfer learning from pretrained models is effective to alleviate label insufficiency in remote sensing (RS) change detection (CD). We explore the use of sema...
Large-factor image super-resolution (SR) is a challenging task due to the high uncertainty and incompleteness of the missing details to be recovered. In remote sensing images, the subpixel spectral mixing and semantic ambiguity of ground objects make this task even more challenging. In this article, we propose a novel method for large-factor SR of...
Remote sensing scene classification is an important yet challenging task. In recent years, the excellent feature representation ability of Convolutional Neural Networks (CNNs) has led to substantial improvements in scene classification accuracy. However, handling resolution variations of remote sensing images is still challenging because CNNs are n...
The proliferation of remote sensing satellites has resulted in a massive amount of remote sensing images. However, due to human and material resource constraints, the vast majority of remote sensing images remain unlabeled. As a result, it cannot be applied to currently available deep learning methods. To fully utilize the remaining unlabeled image...
Existing deep learning based remote sensing images semantic segmentation methods require large-scale labeled datasets. However, the annotation of segmentation datasets is often too time-consuming and expensive. To ease the burden of data annotation, self-supervised representation learning methods have emerged recently. However, the semantic segment...
Training deep learning-based change detection (CD) models heavily relies on large labeled data sets. However, it is time-consuming and labor-intensive to collect large-scale bitemporal images that contain building change, due to both its rarity and sparsity. Contemporary methods to tackle the data insufficiency mainly focus on transformation-based...
Despite the recent progress in deep learning and remote sensing image interpretation, the adaption of a deep learning model between different sources of remote sensing data still remains a challenge. This paper investigates an interesting question: do synthetic data generalize well for remote sensing image applications? To answer this question, we...
Environmental information, like sea-land distribution, plays an important role in detecting ships from remote sensing images. However, the huge scale difference between environments and ship targets makes current CNN-based detection models hard to learn large-scale geographical information and focus on small targets at the same time. We propose an...
