Impact of Radiometric Calibration and Specifications of Spaceborne Optical Imaging Sensors on the Development of Operational Automatic Remote Sensing Image Understanding Systems

2009 ◽  
Vol 2 (2) ◽  
pp. 104-134 ◽  
Author(s):  
Andrea Baraldi
Keyword(s):  
Remote Sensing ◽  
Optical Imaging ◽  
Remote Sensing Image ◽  
Image Understanding ◽  
Radiometric Calibration ◽  
Imaging Sensors

scholarly journals Deep Learning for Remote Sensing Image Understanding

2016 ◽  
Vol 2016 ◽  
pp. 1-2 ◽  
Author(s):  
Liangpei Zhang ◽  
Gui-Song Xia ◽  
Tianfu Wu ◽  
Liang Lin ◽  
Xue Cheng Tai
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Remote Sensing Image ◽  
Image Understanding

scholarly journals Advances in Remote Sensing Extraction of Urban Roads

2021 ◽  
Vol 290 ◽  
pp. 02023
Author(s):  
Liu Shujun
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Traffic Management ◽  
Monitoring And Evaluation ◽  
Research Direction ◽  
Remote Sensing Image ◽  
The United States ◽  
Image Understanding ◽  
Development Planning ◽  
Human Beings

As early as 1970s, the United States has begun the research of remote sensing image processing technology. In recent ten years, the research of road remote sensing extraction in China has also advanced by leaps and bounds. High resolution remote sensing images have been widely used in many fields, such as urban development planning, environmental monitoring and evaluation, and public announcement information services. The main application goal of remote sensing image is to extract the information of the object of interest, then identify it and complete the image understanding. Road is the most important and basic transportation mode of human beings, which provides a lot of support for the development of human civilization. road extraction is important for traffic management, including urban planning, road monitoring, GPS navigation, map updating, image registration, etc. extracting roads from high-resolution remote sensing satellite images is not only a challenging research direction, but also of great practical value.

scholarly journals Description Generation for Remote Sensing Images Using Attribute Attention Mechanism

2019 ◽  
Vol 11 (6) ◽  
pp. 612 ◽  
Author(s):  
Xiangrong Zhang ◽  
Xin Wang ◽  
Xu Tang ◽  
Huiyu Zhou ◽  
Chen Li
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Understanding ◽  
Attention Mechanism ◽  
Semantic Description ◽  
Remote Sensing Images ◽  
Image Captioning ◽  
Proposed Model ◽  
High Level ◽  
The Impact

Image captioning generates a semantic description of an image. It deals with image understanding and text mining, which has made great progress in recent years. However, it is still a great challenge to bridge the “semantic gap” between low-level features and high-level semantics in remote sensing images, in spite of the improvement of image resolutions. In this paper, we present a new model with an attribute attention mechanism for the description generation of remote sensing images. Therefore, we have explored the impact of the attributes extracted from remote sensing images on the attention mechanism. The results of our experiments demonstrate the validity of our proposed model. The proposed method obtains six higher scores and one slightly lower, compared against several state of the art techniques, on the Sydney Dataset and Remote Sensing Image Caption Dataset (RSICD), and receives all seven higher scores on the UCM Dataset for remote sensing image captioning, indicating that the proposed framework achieves robust performance for semantic description in high-resolution remote sensing images.

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