Domain Adaptation in Remote Sensing Image Classification: A Survey

Peng, Jiangtao; Huang, Yung-Fu; Sun, Weiwei; Chen, Na; Ning, Yujie; Du, Qian

doi:10.1109/jstars.2022.3220875

Cited by 76 publications

(12 citation statements)

References 185 publications

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“…Domain adaptation [74] is a unique type of transfer learning that occurs when the data distributions in the source and target domains vary, but the two objectives are the same. Domain adaptation is currently a significant research hotspot in transfer learning.…”

Section: E Transfer Learningmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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Section: E Transfer Learningmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Transfer learning allows acceptable performance of DL models where labelled data is limited and expensive, by leveraging opensource large-scale datasets and available pre-trained model weights [15], but choosing the best transfer learning approach is not trivial. Overrepresentation of outliers [42], sampling bias [41] and model overfitting [40] are some of the most common issues found when training DL models with small datasets. A domain adaptation approach that does not rely on domain-specific labeled train data allows for cheaper and larger train datasets, thus minimizing the impact of the aforementioned issues.…”

Section: Related Workmentioning

confidence: 99%

Ship detection in thermal infrared using paired visible light images and domain adaptation via knowledge distillation

Melo,

Ballan,

van den Broek

et al. 2023

Artificial Intelligence for Security and Defence Applications

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Electro-optical (EO) sensors are essential for surveillance in military and security applications. Recent technological advancements, especially the developments in Deep Learning (DL), have enabled improved object detection and tracking in complex and dynamic environments. Most of this research focuses on readily available visible light (VIS) images. To apply these technologies for Thermal infrared (TIR) imagery, DL networks can be retrained using image data in the TIR domain. However, such a training set with enough samples is not easily available. This paper presents an unsupervised domain adaptation method for ship detection in TIR imagery using paired VIS and TIR images. The proposed method leverages on the pairing of VIS and TIR images and performs domain adaptation using detections in the VIS imagery as ground-truth to provide data for the TIR domain learning. The method performs ship detection from the VIS images using a pretrained convolutional neural network (CNN). These detections are subsequently improved using a tracking algorithm. The proposed TIR object detection model follows a two-stage training process. In the first stage, the model's head is trained, which consists of the regression layers that output the bounding boxes of the detected objects. In the second stage, the model's feature extractor is trained to learn more discriminative features. The method is evaluated on a dataset of recordings at Rotterdam harbor. Experiments demonstrate that the resulting TIR detector performs comparably with its VIS counterpart, in addition to providing reliable detections in adverse environmental conditions where VIS model fails. The proposed method has significant potential for real-world applications, including maritime surveillance.

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“…9 With the development of computer vision technology, the feature representation of remote sensing image scenes has also undergone a transformation from low-level manual design features to high-level deep learning features, gradually moving toward a stronger representation, discriminative, and robust performance. 10 Low-level features consisting of artificial features are commonly used in early high spatial resolution remote sensing image methods. Such features mainly use a lot of engineering skills and domain knowledge to realize the representation of image color, shape, texture, and other information.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, efficient feature extraction and representation of complex content in remote sensing image scenes is a challenge in high-resolution remote sensing image scene classification 9 . With the development of computer vision technology, the feature representation of remote sensing image scenes has also undergone a transformation from low-level manual design features to high-level deep learning features, gradually moving toward a stronger representation, discriminative, and robust performance 10 …”

Section: Introductionmentioning

confidence: 99%

Text guided zero-shot scene classification of high spatial resolution remote sensing images

Liu,

Chen,

Zhou

et al. 2024

J. Appl. Rem. Sens.

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Recently, high spatial resolution remote sensing image scene classification has had a wide range of applications and has become one of the hotspots in the field of remote sensing research. Due to the complexity of the scenes in remote sensing images, it is impossible to annotate all ground object classes at once. To adapt to different application scenarios, high spatial resolution remote sensing image scene classification models need to have zero-shot generalization ability for unseen classes. To improve the zero-shot generalization ability of classification models, the existing methods often start from the perspective of image features, thus ignoring the high-order semantic information in the scene. In fact, the association between higher-order semantic information in the scene is very important for the generalization ability of the classification model. People often use image information and its corresponding higher-order semantic information to complete remote sensing image scene understanding. Therefore, this work proposes a text guided remote sensing image pre-training model for zero-shot classification of high spatial resolution remote sensing image scenes. First, the transformer model is used to extract the embedded features of text and remote sensing images. Then, based on the aligned text and remote sensing image data, a contrast learning method is used to train the model to learn the correspondence between text and image features. After the model training is completed, the nearest neighbor method is used to complete zero-shot classification on the target data. The effectiveness of the proposed method was verified on three remote sensing image scene classification benchmark datasets.

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Domain Adaptation in Remote Sensing Image Classification: A Survey

Cited by 76 publications

References 185 publications

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Ship detection in thermal infrared using paired visible light images and domain adaptation via knowledge distillation

Text guided zero-shot scene classification of high spatial resolution remote sensing images

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