A Sample Update-Based CNN Framework for Object Detection in Large-Area Remote Sensing Images-2019-20

Опубликовано: 22 Май 2026
на канале: PhD Support & Journal Writing
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A Sample Update-Based Convolutional Neural Network Framework for Object Detection in Large-Area Remote Sensing Images-2019-20

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ABSTRACT:
This letter addresses the issue of accurate object detection in large-area remote sensing images. Although many convolutional neural network (CNN)-based object detection models can achieve high accuracy in small image patches, the models perform poorly in large-area images due to the large quantity of false and missing detections that arise from complex backgrounds and diverse groundcover types. To address this challenge, this letter proposes a sample update-based CNN (SUCNN) framework for object detection in large-area remote sensing images. The proposed framework contains two stages. In the first stage, a base model-single-shot multibox detector-is trained with the training data set. In the second stage, artificial composite samples are generated to update the training set. The parameters of the first-stage model are fine-tuned with the updated data set to obtain the second-stage model. The first- and second-stage models are evaluated using the large-area remote sensing image test set. Comparison experiments show the effectiveness and superiority of the proposed SUCNN framework for object detection in large-area remote sensing images.
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