A Training-Free, One-Shot Detection Framework for Geospatial Objects in Remote Sensing Images

Zhang, Tengfei; Sun, Xian; Zhang, Yue; Yan, Menglong; Wang, Yaolin; Wang, Zhirui; Fu, Kun

doi:10.1109/igarss.2019.8898679

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…To annotate a large scale of images, in particular high-resolution RS images, is always an expensive task, and cross checking should be carried out to minimize the risk of false annotation [61]. To address the challenge, few-shot learning becomes a hot topic in RS image classification [62][63][64]. Impressively, Song and Xu explored zero-shot learning for automatic target recognition in synthetic aperture radar (SAR) images [65].…”

Section: Discussionmentioning

confidence: 99%

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

Wen

Jin

et al. 2019

Sensors

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The segmentation of buildings in remote-sensing (RS) images plays an important role in monitoring landscape changes. Quantification of these changes can be used to balance economic and environmental benefits and most importantly, to support the sustainable urban development. Deep learning has been upgrading the techniques for RS image analysis. However, it requires a large-scale data set for hyper-parameter optimization. To address this issue, the concept of “one view per city” is proposed and it explores the use of one RS image for parameter settings with the purpose of handling the rest images of the same city by the trained model. The proposal of this concept comes from the observation that buildings of a same city in single-source RS images demonstrate similar intensity distributions. To verify the feasibility, a proof-of-concept study is conducted and five fully convolutional networks are evaluated on five cities in the Inria Aerial Image Labeling database. Experimental results suggest that the concept can be explored to decrease the number of images for model training and it enables us to achieve competitive performance in buildings segmentation with decreased time consumption. Based on model optimization and universal image representation, it is full of potential to improve the segmentation performance, to enhance the generalization capacity, and to extend the application of the concept in RS image analysis.

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Section: Discussionmentioning

confidence: 99%

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

Wen

Jin

et al. 2019

Sensors

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“…On the other hand, object detection task focuses on objects rather than the whole image and therefore is more challenging. [34] is a training-free design in remote sensing and target at sewage treatment plant and airport detections. Nevertheless, it is not able to be trained to be generalized to detect other objects and therefore its applications are severely restricted.…”

Section: Related Workmentioning

confidence: 99%

Few-Shot Object Detection With Self-Adaptive Attention Network for Remote Sensing Images

Xiao

Xue

et al. 2021

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

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In remote sensing field, there are many applications of object detection in recent years, which demands a great number of labeled data. However, we may be faced with some cases where only limited data are available. In this paper, we proposed a fewshot object detector which is designed for detecting novel objects provided with only a few examples. Particularly, in order to fit the object detection settings, our proposed few-shot detector concentrates on the relations that lie in the level of objects instead of the full image with the assistance of Self-Adaptive Attention Network (SAAN). The SAAN can fully leverage the object-level relations through a relation GRU unit and simultaneously attach attention on object features in a self-adaptive way according to the object-level relations to avoid some situations where the additional attention is useless or even detrimental. Eventually, the detection results are produced from the features that are added with attention and thus are able to be detected simply. The experiments demonstrate the effectiveness of the proposed method in few-shot scenes.

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Few-shot remote sensing image scene classification: Recent advances, new baselines, and future trends

Qiu,

Zhang,

Tong

et al. 2024

ISPRS Journal of Photogrammetry and Remote Sensing

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A Training-Free, One-Shot Detection Framework for Geospatial Objects in Remote Sensing Images

Cited by 7 publications

References 4 publications

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

One View Per City for Buildings Segmentation in Remote-Sensing Images via Fully Convolutional Networks: A Proof-of-Concept Study

Few-Shot Object Detection With Self-Adaptive Attention Network for Remote Sensing Images

Few-shot remote sensing image scene classification: Recent advances, new baselines, and future trends

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