An enhanced deep convolutional neural network for densely packed objects detection in remote sensing images

2017 ◽  
Author(s):  
Zhipeng Deng ◽  
Lin Lei ◽  
Hao Sun ◽  
Huanxin Zou ◽  
Shilin Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Remote Sensing Images ◽  
Objects Detection

scholarly journals Automatic Detection of Flood Using Remote Sensing Images

2020 ◽  
Vol 02 (01) ◽  
pp. 11-26 ◽  
Author(s):  
Amith Chandrakant Chawan ◽  
Vaibhav K Kakade ◽  
Jagannath K Jadhav
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Remote Sensing Images ◽  
Hazardous Area ◽  
High Resolution Images ◽  
Flood Detection

Remote sensing imaging (RSI) technology has recently been identified as an effective photogrammetric data acquisition platform to rapidly provide high resolution images due to its profitability, its ability to fly at low altitude and the ability to analysis in dangerous areas. The various kinds of classification techniques are have been used for flood extent mapping for finding the flood affected region, but based on the color region based analysis the classified hazardous area has very complex. Due to over the above issues in this work there significant enhancements have appeared in the classification of remote sensing images using Contiguous Deep Convolutional Neural Network (CDCNN).In the flood detection system the four different kinds of process like preprocessing, segmentation, feature extraction and the Contiguous Deep Convolutional Neural Network (CDCNN) has been executed for identifying the flood defected region. This works also investigates and compare with the possible methods with the proposed CDCNN for accurately identified by the Classification details of the RSI

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