Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach

Ajadi, Olaniyi A.; Meyer, Franz J.; Webley, P. W.

doi:10.3390/rs8060482

Cited by 57 publications

(38 citation statements)

References 32 publications

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“…In addition, the obtained no-change set should have a correlation coefficient greater than 0.9 [12,13]. In this study, the center coordinate of the water cluster in the scattergram was (5,5), and the center coordinate of the land surface in the scattergram was (71, 88). From these two coordinates, the initial normalization coefficients were acquired: a 4 0 = 1.2575; and b 4 0 = −1.2878.…”

Section: Automatic Detection Of No-change Pixelmentioning

confidence: 99%

“…As multi-temporal images are often captured by different sensors under variable atmospheric conditions, degrees of solar illumination, and viewing angles, radiometric normalization is required to remove radiometric differences and make the images comparable [5][6][7]. There are two methods for radiometric normalizations: the absolute method and the relative method [8].…”

Section: Introductionmentioning

confidence: 99%

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Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

Seo

Kim

et al. 2017

Remote Sensing

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Abstract:Efforts have been made to detect both naturally occurring and anthropogenic changes to the Earth's surface by using satellite remote sensing imagery. There is a need to maintain the homogeneity of radiometric and phenological conditions to ensure accuracy in change detection, but images to assess long-term changes in time-series data that satisfy such conditions are difficult to obtain. For this reason, image normalization is essential. In particular, the normalizing compositive conditions require nonlinear modeling, and random forest (RF) techniques can be utilized for this normalization. This study employed Landsat-5 Thematic Mapper satellite images with temporal, radiometric and phenological differences, and obtained Radiometric Control Set Samples by selecting no-change pixels between the subject image and reference image using scattergrams. In the obtained no-change regions, RF regression was modeled, and normalized images were obtained. Next, normalization performance was evaluated by comparing the results against the following conventional linear regression methods: mean-standard deviation regression, simple regression, and no-change regression. The normalization performance of RF regression was much higher. In addition, for an additional usefulness evaluation in normalization, the normalization performance was compared with other nonlinear ensemble regressions, i.e. Adaptive Boosting regression and Stochastic Gradient Boosting regression, which confirmed that the normalization performance of RF regression was significantly higher. In other words, it was found to be highly useful for normalization when compared to other nonlinear ensemble regressions. Finally, as a result of performing change detection, normalized subject images generated by RF regression showed the highest accuracy, which indicated that the proposed method (where the image was normalized using RF regression) may be useful in change detection between multi-temporal image datasets.

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Section: Automatic Detection Of No-change Pixelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

Seo

Kim

et al. 2017

Remote Sensing

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“…Equation (23) shows that the system can literally increase the Doppler frequency by adjusting the radial velocity of the transmitter. Therefore, it has some flexibility even for a very low speed (almost stationary) target.…”

Section: Practical Realization Of Fsr With Rotational Scanning Transmmentioning

confidence: 99%

“…The task is to monitor the change in the received signal due to the target movement on the ground surface. An established method, such as Coherent Change Detection (CCD), was proposed for surface displacement monitoring as applied in SAR [22][23][24]. The CCD technique correlates consecutive SAR images at the phase level and relates the phase changes to the actual surface change.…”

Section: Moving Target Detection and Localizationmentioning

confidence: 99%

Joint Time-Frequency Signal Processing Scheme in Forward Scattering Radar with a Rotational Transmitter

et al. 2016

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This paper explores the concept of a Forward Scattering Radar (FSR) system with a rotational transmitter for target detection and localization. Most of the research and development in FSR used a fixed dedicated transmitter; therefore, the detection of stationary and slow moving target is very difficult. By rotating the transmitter, the received signals at the receiver contain extra information carried by the Doppler due to the relative movement of the transmitter-target-receiver. Hence, rotating the transmitter enhances the detection capability especially for a stationary and slow-moving target. In addition, it increases the flexibility of the transmitter to control the signal direction, which broadens the coverage of FSR networks. In this paper, a novel signal processing for the new mode of FSR system based on the signal's joint time-frequency is proposed and discussed. Additionally, the concept of the FSR system with the rotational transmitter is analyzed experimentally for the detection and localization of a stationary target, at very low speed and a low profile target crossing the FSR baseline. The system acts as a virtual fencing of a remote sensor for area monitoring. The experimental results show that the proposed mode with the new signal processing scheme can detect a human intruder. The potential applications for this system could be used for security and border surveillance, debris detection on an airport runway, ground aerial monitoring, intruder detection, etc.

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“…Since the first overviews regarding change detection in the 1960s [3], many works in this discipline have been conducted using remote sensing data. Among the recent works, we can cite the monitoring of arid environments [4], shorelines [5] and forests or woodlands [6][7][8][9] as well as detection of urban expansion [10], changes in buildings structure [11], changes in submerged sea grass biomass [12], mapping of landslides [13], damage to cultural heritage sites [14] and the proposition of new methodologies [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Examining Multi-Legend Change Detection in Amazon with Pixel and Region Based Methods

et al. 2017

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Post-classification comparison is one of the most widely used change detection methods. However, it presents several operational problems that are often ignored, such as the occurrence of impossible transitions, difficulties in accuracy assessment and results not accurate enough for the purpose. This work aims to evaluate post-classification comparison change detection results obtained from LANDSAT5/TM data in a region of the Brazilian Amazon, using three legends in different levels of detail and both pixel wise and region based classifiers. A distinctive characteristic of the used approach is that each change mapping is the result of the combination of 100 land cover classifications for each date, obtained using varied training samples. This approach allowed to account for the training samples choice into the methodology, as well as the construction of confidence mappings. We presented and discussed different approaches for evaluating change results, such as the likelihood of land cover transitions occurring within the study area and time gap, the use of rectangular matrices to incorporate the occurrence of impossible or non evaluable changes and classification uncertainty. In general, change mappings obtained from region based classifications showed better results than the ones obtained from pixel based classifications. Globally, the use of region based approaches, in contrast to pixel based ones, led to an increase in accuracy of 15.5% for the change mapping from the most detailed legend, 7.8% for the one with the legend with intermediate level of detail and 3.6% for the less detailed one. In addition, individual transitions between land cover classes were better identified using region based approaches, with the exception of transitions from a non agriculture class to an agricultural one. The proposed quality mappings are useful to help to evaluate the change mappings, mainly in legend levels with higher level of detail and if reference samples are unreliable or unavailable. It was possible to access, in a spatially explicit way, that at least 29.0% of the pixel based change mapping and 21.9% of the region based one from the most detailed legend were erroneous classified, without ground truth information on the evaluated date. These values decreased to 0.5% and 1.4% (respectively the pixel and region based approaches) for results with the legend with the intermediate level of detail and are non existent in the results from the less detailed legend. The more generalized the legend (lower number of classes), the most similar are the accuracy of region and pixel based change mappings. These accuracy values also increase as fewer classes are considered in the legend, since similar classes are assembled during clustering, which reduces the overlap between groups. However, this accuracy is still low for operational purposes in areas with few changes, even considering the very high accuracy of the land cover classifications used to generate the change mappings (land cover classification with Overall Accur...

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Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach

Cited by 57 publications

References 32 publications

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

Joint Time-Frequency Signal Processing Scheme in Forward Scattering Radar with a Rotational Transmitter

Examining Multi-Legend Change Detection in Amazon with Pixel and Region Based Methods

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