Multi-scale Dense Object Detection in Remote Sensing Imagery Based on Keypoints

2020 ◽  
pp. 104-116
Author(s):  
Qingxiang Guo ◽  
Yingjian Liu ◽  
Haoyu Yin ◽  
Yue Li ◽  
Chaohui Li
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Remote Sensing Imagery ◽  
Multi Scale

scholarly journals Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network

2019 ◽  
Vol 11 (7) ◽  
pp. 755 ◽  
Author(s):  
Xiaodong Zhang ◽  
Kun Zhu ◽  
Guanzhou Chen ◽  
Xiaoliang Tan ◽  
Lifei Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Large Scale ◽  
Training Data ◽  
Validation Dataset ◽  
Remote Sensing Imagery ◽  
Scale Feature ◽  
Multi Scale ◽  
Feature Pyramid

Object detection on very-high-resolution (VHR) remote sensing imagery has attracted a lot of attention in the field of image automatic interpretation. Region-based convolutional neural networks (CNNs) have been vastly promoted in this domain, which first generate candidate regions and then accurately classify and locate the objects existing in these regions. However, the overlarge images, the complex image backgrounds and the uneven size and quantity distribution of training samples make the detection tasks more challenging, especially for small and dense objects. To solve these problems, an effective region-based VHR remote sensing imagery object detection framework named Double Multi-scale Feature Pyramid Network (DM-FPN) was proposed in this paper, which utilizes inherent multi-scale pyramidal features and combines the strong-semantic, low-resolution features and the weak-semantic, high-resolution features simultaneously. DM-FPN consists of a multi-scale region proposal network and a multi-scale object detection network, these two modules share convolutional layers and can be trained end-to-end. We proposed several multi-scale training strategies to increase the diversity of training data and overcome the size restrictions of the input images. We also proposed multi-scale inference and adaptive categorical non-maximum suppression (ACNMS) strategies to promote detection performance, especially for small and dense objects. Extensive experiments and comprehensive evaluations on large-scale DOTA dataset demonstrate the effectiveness of the proposed framework, which achieves mean average precision (mAP) value of 0.7927 on validation dataset and the best mAP value of 0.793 on testing dataset.

Multi-scale object detection in remote sensing imagery with convolutional neural networks

2018 ◽  
Vol 145 ◽  
pp. 3-22 ◽  
Author(s):  
Zhipeng Deng ◽  
Hao Sun ◽  
Shilin Zhou ◽  
Juanping Zhao ◽  
Lin Lei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Remote Sensing Imagery ◽  
Multi Scale

Multi-Scale Spatial and Channel-wise Attention for Improving Object Detection in Remote Sensing Imagery

2020 ◽  
Vol 17 (4) ◽  
pp. 681-685 ◽  
Author(s):  
Jie Chen ◽  
Li Wan ◽  
Jingru Zhu ◽  
Gang Xu ◽  
Min Deng
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Remote Sensing Imagery ◽  
Multi Scale

scholarly journals A Multi-Scale Spatial Attention Region Proposal Network for High-Resolution Optical Remote Sensing Imagery

2021 ◽  
Vol 13 (17) ◽  
pp. 3362
Author(s):  
Ruchan Dong ◽  
Licheng Jiao ◽  
Yan Zhang ◽  
Jin Zhao ◽  
Weiyan Shen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Spatial Attention ◽  
Recall Rate ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Remote Sensing Imagery ◽  
Multi Scale ◽  
Backbone Network

Deep convolutional neural networks (DCNNs) are driving progress in object detection of high-resolution remote sensing images. Region proposal generation, as one of the key steps in object detection, has also become the focus of research. High-resolution remote sensing images usually contain various sizes of objects and complex background, small objects are easy to miss or be mis-identified in object detection. If the recall rate of region proposal of small objects and multi-scale objects can be improved, it will bring an improvement on the performance of the accuracy in object detection. Spatial attention is the ability to focus on local features in images and can improve the learning efficiency of DCNNs. This study proposes a multi-scale spatial attention region proposal network (MSA-RPN) for high-resolution optical remote sensing imagery. The MSA-RPN is an end-to-end deep learning network with a backbone network of ResNet. It deploys three novel modules to fulfill its task. First, the Scale-specific Feature Gate (SFG) focuses on features of objects by processing multi-scale features extracted from the backbone network. Second, the spatial attention-guided model (SAGM) obtains spatial information of objects from the multi-scale attention maps. Third, the Selective Strong Attention Maps Model (SSAMM) adaptively selects sliding windows according to the loss values from the system’s feedback, and sends the windowed samples to the spatial attention decoder. Finally, the candidate regions and their corresponding confidences can be obtained. We evaluate the proposed network in a public dataset LEVIR and compare with several state-of-the-art methods. The proposed MSA-RPN yields a higher recall rate of region proposal generation, especially for small targets in remote sensing images.

Application of multi-scale remote sensing imagery to detection and hazard analysis

2012 ◽  
Vol 65 (3) ◽  
pp. 2241-2252 ◽  
Author(s):  
C. C. Liu ◽  
Y. Y. Chen ◽  
C. W. Chen
Keyword(s):  
Remote Sensing ◽  
Hazard Analysis ◽  
Remote Sensing Imagery ◽  
Multi Scale
Sign in / Sign up

Export Citation Format

Share Document