Real-time ortho-rectification for remote-sensing images

2018 ◽  
Vol 40 (5-6) ◽  
pp. 2451-2465 ◽  
Author(s):  
Guoqing Zhou ◽  
Rongting Zhang ◽  
Dianjun Zhang ◽  
Jingjin Huang ◽  
Oktay Baysal
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Remote Sensing Images

scholarly journals Real-Time Semantic Segmentation of Remote Sensing Images Based on Bilateral Attention Refined Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 28349-28360
Author(s):  
Jiali Cai ◽  
Chunjuan Liu ◽  
Haowen Yan ◽  
Xiaosuo Wu ◽  
Wanzhen Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Semantic Segmentation ◽  
Remote Sensing Images

scholarly journals Locally Oriented Scene Complexity Analysis Real-Time Ocean Ship Detection from Optical Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3799 ◽  
Author(s):  
Yin Zhuang ◽  
Baogui Qi ◽  
He Chen ◽  
Fukun Bi ◽  
Lianlin Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Cloud Cover ◽  
Complexity Analysis ◽  
Response Analysis ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Feature Clustering ◽  
Ship Detection ◽  
Clustering Model

Due to strong ocean waves, broken clouds, and extensive cloud cover interferences, ocean ship detection performs poorly when using optical remote sensing images. In addition, it is a challenge to detect small ships on medium resolution optical remote sensing that cover a large area. In this paper, in order to balance the requirements of real-time processing and high accuracy detection, we proposed a novel ship detection framework based on locally oriented scene complexity analysis. First, the proposed method can separate a full image into two types of local scenes (i.e., simple or complex local scenes). Next, simple local scenes would utilize the fast saliency model (FSM) to rapidly complete candidate extraction, and for complex local scenes, the ship feature clustering model (SFCM) will be applied to achieve refined detection against severe background interferences. The FSM considers a fusion enhancement image as an input of the pulse response analysis in the frequency domain to achieve rapid ship detection in simple local scenes. Next, the SFCM builds the descriptive model of the ship feature clustering algorithm to ensure the detection performance on complex local scenes. Extensive experiments on SPOT-5 and GF-2 ocean optical remote sensing images show that the proposed ship detection framework has better performance than the state-of-the-art methods, and it addresses the tricky problem of real-time ocean ship detection under strong waves, broken clouds, extensive cloud cover, and ship fleet interferences. Finally, the proposed ocean ship detection framework is demonstrated on an onboard processing hardware.

Real-time cheating immune secret sharing for remote sensing images

2020 ◽  
Author(s):  
Shivendra Shivani ◽  
Subhash Chandra Patel ◽  
Vinay Arora ◽  
Bhisham Sharma ◽  
Alireza Jolfaei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Secret Sharing ◽  
Remote Sensing Images
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