Development of change detection algorithm using high resolution SAR complex image

2015 ◽  
Author(s):  
Ah-Leum Kim ◽  
Ki-Woong Lee ◽  
Bum-Seung Kim ◽  
Woo-Kyung Lee ◽  
Jisen Baek
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Detection Algorithm ◽  
Complex Image

Change detection algorithm based on amplitude statistical distribution for high resolution SAR image

2015 ◽  
Vol 31 (3) ◽  
pp. 227-244 ◽  
Author(s):  
Kiwoong Lee ◽  
Seoli Kang ◽  
Ahleum Kim ◽  
Kyungmin Song ◽  
Wookyung Lee
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Detection Algorithm ◽  
Statistical Distribution ◽  
Sar Image

scholarly journals Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery

2018 ◽  
Vol 8 (10) ◽  
pp. 1785 ◽  
Author(s):  
Wahyu Wiratama ◽  
Jongseok Lee ◽  
Sang-Eun Park ◽  
Donggyu Sim
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Temporal Change ◽  
Area Under The Curve ◽  
Detection Algorithm ◽  
Feature Maps ◽  
Convolutional Network ◽  
Learning Stage ◽  
Convolutional Networks ◽  
Local Characteristics

This paper presents a robust change detection algorithm for high-resolution panchromatic imagery using a proposed dual-dense convolutional network (DCN). In this work, a joint structure of two deep convolutional networks with dense connectivity in convolution layers is designed in order to accomplish change detection for satellite images acquired at different times. The proposed network model detects pixel-wise temporal change based on local characteristics by incorporating information from neighboring pixels. Dense connection in convolution layers is designed to reuse preceding feature maps by connecting them to all subsequent layers. Dual networks are incorporated by measuring the dissimilarity of two temporal images. In the proposed algorithm for change detection, a contrastive loss function is used in a learning stage by running over multiple pairs of samples. According to our evaluation, we found that the proposed framework achieves better detection performance than conventional algorithms, in area under the curve (AUC) of 0.97, percentage correct classification (PCC) of 99%, and Kappa of 69, on average.

A Novel Change Detection Method Using Independent Component Analysis and Oriented-Object Method

2014 ◽  
Vol 548-549 ◽  
pp. 633-636
Author(s):  
Xiao Chun Li ◽  
Chun Yang Jia ◽  
Wei Hua Li
Keyword(s):  
High Resolution ◽  
Independent Component Analysis ◽  
Change Detection ◽  
Component Analysis ◽  
Detection Algorithm ◽  
Independent Component ◽  
Input Vector ◽  
Detection Methods ◽  
Discrete Wavelet ◽  
Oriented Object

hrough analyzing problems brought on change detection methods of high-resolution remote sensing images, a novel change detection algorithm is proposed. First, feature images of image’s objects extracted using oriented-object method serve as data of input vector to estimate sub-space for Independent Component Analysis(ICA), which can improve effect of noise suppression, simultaneously, a new algorithm using self-adapted weight is proposed in order to extract image’s object, which optimizes processing method on oriented-object deeply；new partitioning scheme using undecimated discrete wavelet transform(UDWT) overcomes effectively prominent problem which shrinking of the size of input vector becomes leads to unprecisely estimation of sub-space for ICA. Compared with typical algorithm, such as ICA and UDWT, simulation results show that new algorithm improves robust and veracity of change detection for high-resolution images greatly.

Sign in / Sign up

Export Citation Format

Share Document