scholarly journals A Change Detection Algorithm for Retrieving High-Resolution Soil Moisture From SMAP Radar and Radiometer Observations

2009 ◽  
Vol 47 (12) ◽  
pp. 4125-4131 ◽  
Author(s):  
M. Piles ◽  
D. Entekhabi ◽  
A. Camps
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Change Detection ◽  
Detection Algorithm

scholarly journals Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

2011 ◽  
Vol 8 (6) ◽  
pp. 10333-10367
Author(s):  
J. Van doninck ◽  
J. Peters ◽  
H. Lievens ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Change Detection ◽  
Incidence Angle ◽  
Arable Land ◽  
Correlation Coefficients ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Second Phase ◽  
Wide Swath

Abstract. A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS Scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing vegetation and soil moisture mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

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 Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

2012 ◽  
Vol 16 (3) ◽  
pp. 773-786 ◽  
Author(s):  
J. Van doninck ◽  
J. Peters ◽  
H. Lievens ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Change Detection ◽  
Incidence Angle ◽  
Arable Land ◽  
Correlation Coefficients ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Second Phase ◽  
Wide Swath

Abstract. A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing surface properties mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

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.

scholarly journals A New Soil Moisture Retrieval Algorithm from the L-Band Passive Microwave Brightness Temperature Based on the Change Detection Principle

2020 ◽  
Vol 12 (8) ◽  
pp. 1303
Author(s):  
Xingming Zheng ◽  
Zhuangzhuang Feng ◽  
Hongxin Xu ◽  
Yanlong Sun ◽  
Lei Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Change Detection ◽  
Brightness Temperature ◽  
Detection Algorithm ◽  
Passive Microwave ◽  
Theoretical Simulation ◽  
Microwave Brightness Temperature ◽  
Input Parameters ◽  
L Band

The launch of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) satellites has led to the development of a series of L-band soil moisture retrieval algorithms. In these algorithms, many input parameters (such as leaf area index and soil texture) and empirical coefficients (such as roughness coefficient (hP, NRP) and crop structure parameter (bP, ttP)) are needed to calculate surface soil moisture (SSM) from microwave brightness temperature. Many previous studies have focused on how to determine the value of these coefficients and input parameters. Nevertheless, it can be difficult to obtain their ‘real’ values with low uncertainty across large spatial scales. To avoid this problem, a passive microwave remote sensing SSM inversion algorithm based on the principle of change detection was proposed and tested using theoretical simulation and a field SSM dataset for an agricultural area in northeastern China. This algorithm was initially used to estimate SSM for radar remote sensing. First, theoretical simulation results were used to confirm the linear relationship between the change rates for SSM and surface emissivity, for both H and V polarization. This demonstrated the reliability of the change detection algorithm. Second, minimum emissivity (or the difference between maximum emissivity and minimum emissivity) was modeled with a linear relationship between vegetation water content, derived from a three-year (2016–2018) SMAP L3 SSM dataset. Third, SSM values estimated by the change detection algorithm were in good agreement with SMAP L3 SSM and field SSM, with RMSE values ranging from 0.015~0.031 cm3/cm3 and 0.038~0.051 cm3/cm3, respectively. The V polarization SSM accuracy was higher than H polarization and combined H and V polarization accuracy. The retrieved SSM error from the change detection algorithm was similar to SMAP SSM due to errors inherited from the training dataset. The SSM algorithm proposed here is simple in form, has fewer input parameters, and avoids the uncertainty of input parameters. It is very suitable for global applications and will provide a new algorithm option for SSM estimation from microwave brightness temperature.

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.

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