New Region of Interest Coding for Remote Sensing Image Based on Multiple Bitplanes Up-down Shift

2006 ◽  
Author(s):  
Li-bao Zhang ◽  
Xianchuan Yu
Keyword(s):  
Remote Sensing ◽  
Region Of Interest ◽  
Remote Sensing Image ◽  
Region Of Interest Coding

Object Detection with an Improved Level Set Method

2013 ◽  
Vol 303-306 ◽  
pp. 1060-1066
Author(s):  
Hui Cao ◽  
Bo Cheng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Level Set ◽  
Region Of Interest ◽  
Remote Sensing Image ◽  
Aerial Images ◽  
Important Research ◽  
Speed Up ◽  
Level Set Formulation

Object detection is quite an important research in remote sensing image analysis. In this paper, we propose an edge and region based model for high resolution remote sensing image segmentation with level set formulation. Our method firstly made an image enhancement based on ROI (Region of Interest). By introducing the edge speed-up function, we can save time through decreasing the iterations and get a flexible segmentation considering the complexity of high resolution remote sensing image. Our method has been preliminarily applied to QuickBird and aerial images.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

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