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IEEE Transactions on Geoscience and Remote Sensing
Scopus
ISSN 0196-2892 · Institute of Electrical and Electronics Engineers · 28 articles in the index
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ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model
Convolutional neural networks (CNNs) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN is constrained by a limited receptive field that may hinder their ability to capture broader spatial…
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MambaHSI: Spatial–Spectral Mamba for Hyperspectral Image Classification
Transformer has been extensively explored for hyperspectral image (HSI) classification. However, transformer poses challenges in terms of speed and memory usage because of its quadratic computational complexity. Recently, the Mamba model has emerged as a promising approach, which has strong…
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SCTransNet: Spatial-Channel Cross Transformer Network for Infrared Small Target Detection
Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. However, largely overlooking effective global information modeling, existing techniques struggle when the target has high similarities with the background. We present…
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SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
Accurately and timely detecting multiscale small objects that contain tens of pixels from remote sensing images (RSI) remains challenging. Most of the existing solutions primarily design complex deep neural networks to learn strong feature representations for objects separated from the background…
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Changer: Feature Interaction is What You Need for Change Detection
Change detection is an important tool for long-term earth observation missions. It takes bi-temporal images as input and predicts “where” the change has occurred. Different from other dense prediction tasks, a meaningful consideration for change detection is the interaction between bi-temporal…
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Multimodal Fusion Transformer for Remote Sensing Image Classification
Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs). As a result, many researchers have tried to incorporate ViTs in hyperspectral image (HSI) classification tasks. To achieve…
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MTU-Net: Multilevel TransUNet for Space-Based Infrared Tiny Ship Detection
Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by Earth-orbiting satellites. Due to the extremely large image coverage area (e.g., thousands of square kilometers), candidate targets in these images are much smaller, dimer, and more changeable than…
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Multiscale Diff-Changed Feature Fusion Network for Hyperspectral Image Change Detection
For hyperspectral image (HSI) change detection (CD), multiscale features are usually used to construct the detection models. However, the existing studies only consider the multiscale features containing changed and unchanged components, which is difficult to represent the subtle changes between…
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Anchor-Free Oriented Proposal Generator for Object Detection
Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them. However, the horizontal boxes are inclined to get small Intersection-over-Unions (IoUs)…
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An Unsupervised Domain Adaptation Method Towards Multi-Level Features and Decision Boundaries for Cross-Scene Hyperspectral Image Classification
Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-scene classification, samples between source and target scenes are not drawn from the independent and identical distribution, resulting in significant performance degradation. To tackle this issue, a novel…
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Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
Semantic change detection (SCD) extends the multiclass change detection (MCD) task to provide not only the change locations but also the detailed land-cover/land-use (LCLU) categories before and after the observation intervals. This fine-grained semantic change information is very useful in many…
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Building Extraction With Vision Transformer
As an important carrier of human productive activities, the extraction of buildings is not only essential for urban dynamic monitoring but also necessary for suburban construction inspection. Nowadays, accurate building extraction from remote sensing images remains a challenge due to the complex…
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An Empirical Study of Remote Sensing Pretraining
Deep learning has largely reshaped remote sensing (RS) research for aerial image understanding and made a great success. Nevertheless, most of the existing deep models are initialized with the ImageNet pretrained weights since natural images inevitably present a large domain gap relative to aerial…
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Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images
Semantic segmentation of remote sensing images plays an important role in a wide range of applications, including land resource management, biosphere monitoring, and urban planning. Although the accuracy of semantic segmentation in remote sensing images has been increased significantly by deep…
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Remote Sensing Image Change Detection With Transformers
Modern change detection (CD) has achieved remarkable success by the powerful discriminative ability of deep convolutions. However, high-resolution remote sensing CD remains challenging due to the complexity of objects in the scene. Objects with the same semantic concept may show distinct spectral…
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Attentional Local Contrast Networks for Infrared Small Target Detection
To mitigate the issue of minimal intrinsic features for pure data-driven methods, in this article, we propose a novel model-driven deep network for infrared small target detection, which combines discriminative networks and conventional model-driven methods to make use of both labeled data and the…
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CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote-Sensing Images
Object detection in optical remote-sensing images is an important and challenging task. In recent years, the methods based on convolutional neural networks (CNNs) have made good progress. However, due to the large variation in object scale, aspect ratio, as well as the arbitrary orientation, the…
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LANet: Local Attention Embedding to Improve the Semantic Segmentation of Remote Sensing Images
The trade-off between feature representation power and spatial localization accuracy is crucial for the dense classification/semantic segmentation of remote sensing images (RSIs). High-level features extracted from the late layers of a neural network are rich in semantic information, yet have…
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Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
In this article, we propose an efficient and effective framework to fuse hyperspectral and light detection and ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to…
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Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening
The fusion of high spatial resolution panchromatic (PAN) data with simultaneously acquired multispectral (MS) data with the lower spatial resolution is a hot topic, which is often called pansharpening. In this article, we exploit the combination of machine learning techniques and fusion schemes…