Target heat-map network: An end-to-end deep network for target detection in remote sensing images

2019 ◽  
Vol 331 ◽  
pp. 375-387 ◽  
Author(s):  
Huai Chen ◽  
Libao Zhang ◽  
Jie Ma ◽  
Jue Zhang
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Remote Sensing Images ◽  
Heat Map ◽  
Deep Network ◽  
End To End

Rotation and scale invariant target detection in optical remote sensing images based on pose-consistency voting

2016 ◽  
Vol 76 (12) ◽  
pp. 14461-14483 ◽  
Author(s):  
Yudong Lin ◽  
Hongjie He ◽  
Heng-Ming Tai ◽  
Fan Chen ◽  
Zhongke Yin
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Scale Invariant

scholarly journals Effective Complex Airport Object Detection in Remote Sensing Images Based on Improved End-to-End Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 172652-172663
Author(s):  
Yongsai Han ◽  
Shiping Ma ◽  
Yuelei Xu ◽  
Linyuan He ◽  
Shuai Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Remote Sensing Images ◽  
End To End

scholarly journals Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

2020 ◽  
Author(s):  
Jakaria Rabbi ◽  
Nilanjan Ray ◽  
Matthias Schubert ◽  
Subir Chowdhury ◽  
Dennis Chao
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Super Resolution ◽  
Detection Performance ◽  
Superior Performance ◽  
Single Shot ◽  
Small Object ◽  
Remote Sensing Images ◽  
Low Resolution ◽  
End To End

The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for the small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the GAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on car overhead with context and oil and gas storage tank (created by us) data sets show superior performance of our method compared to the standalone state-of-the-art object detectors.

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