Coherent Change Detection Under a Forest Canopy

Pincus, P.; Preiss, M.

doi:10.1109/radar.2018.8557223

Cited by 4 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, the values of the covariance matrices are generally unknown, and thus, in practice, sub-optimal tests are implemented where the unknown parameters are measured directly through the data with their maximum likelihood estimates (MLEs) to form a generalized likelihood test (e.g., see [71]). Due to the complexity of these optimal tests and the need to estimate the mentioned model parameters, several approximate unsupervised/supervised CD approaches have been proposed in the literature (among others, see [14,45,[89][90][91][92][93][94]). Overall, they can be distinguished in the two general categories of the methods based exclusively on the analysis of the amplitude of SAR backscattered returns (incoherent approaches) and those that also consider the phase of the radar backscattered signals (coherent strategies).…”

Section: Fundamentals Of Coherent and Incoherent Change Detection Met...mentioning

confidence: 99%

“…This circumstance can be explained by the fact that SAR images are corrupted by the presence of speckle noise [32][33][34]. Despite its complexity, SAR images in change detection [35][36][37][38][39][40][41][42][43][44][45] were attractive from the operational viewpoint since SAR sensors are active instruments operating in any atmospheric and sunlight conditions. This means that they can monitor geographical areas recurrently, even while covered by clouds and during periods when solar illumination is severely limited.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

et al. 2022

View full text Add to dashboard Cite

This work aims to clarify the potential of incoherent and coherent change detection (CD) approaches for detecting and monitoring ground surface changes using sequences of synthetic aperture radar (SAR) images. Nowadays, the growing availability of remotely sensed data collected by the twin Sentinel-1A/B sensors of the European (EU) Copernicus constellation allows fast mapping of damage after a disastrous event using radar data. In this research, we address the role of SAR (amplitude) backscattered signal variations for CD analyses when a natural (e.g., a fire, a flash flood, etc.) or a human-induced (disastrous) event occurs. Then, we consider the additional pieces of information that can be recovered by comparing interferometric coherence maps related to couples of SAR images collected between a principal disastrous event date. This work is mainly concerned with investigating the capability of different coherent/incoherent change detection indices (CDIs) and their mutual interactions for the rapid mapping of “changed” areas. In this context, artificial intelligence (AI) algorithms have been demonstrated to be beneficial for handling the different information coming from coherent/incoherent CDIs in a unique corpus. Specifically, we used CDIs that synthetically describe ground surface changes associated with a disaster event (i.e., the pre-, cross-, and post-disaster phases), based on the generation of sigma nought and InSAR coherence maps. Then, we trained a random forest (RF) to produce CD maps and study the impact on the final binary decision (changed/unchanged) of the different layers representing the available synthetic CDIs. The proposed strategy was effective for quickly assessing damage using SAR data and can be applied in several contexts. Experiments were conducted to monitor wildfire’s effects in the 2021 summer season in Italy, considering two case studies in Sardinia and Sicily. Another experiment was also carried out on the coastal city of Houston, Texas, the US, which was affected by a large flood in 2017; thus, demonstrating the validity of the proposed integrated method for fast mapping of flooded zones using SAR data.

show abstract

Section: Fundamentals Of Coherent and Incoherent Change Detection Met...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This is explained by the fact that the phase information acquired by the SAR system is very sensitive to subtle changes. Generally, the change of half of the wavelength level is large enough to cause complete decorrelation [ 18 ]. Considering the influence of noise and other factors, CCD can at least detect changes in the wavelength scale, which means that the CCD method plays an important role in detecting human activities and other small changes.…”

Section: Introductionmentioning

confidence: 99%

A Coherence Improvement Method Based on Sub-Aperture InSAR for Human Activity Detection

Wang

Xiang

et al. 2021

Sensors

View full text Add to dashboard Cite

Human activity detection plays an important role in social security monitoring. Since human activity is very weak, it is necessary to employ the repeat-pass Interferometric Synthetic Aperture Radar (InSAR) technique to detect the potential activity between two data acquisitions; a high level of coherence is required for detection. With the object of detecting human activity of interest, this paper presents a coherence improvement approach based on sub-aperture InSAR for human activity detection. Different sub-apertures contain different scattering information of the target, as they represent the backscatter of the target from a different range of angles. Integrating corresponding sub-aperture interferometric results can improve the coherence between two complex images compared to the entire synthetic aperture, as well as removing a little disturbance in some circumstances. To validate the method presented in this paper, the actual airborne Ka-band frequency modulated continuous wave (FMCW) InSAR data acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS) are utilized. The experimental results demonstrate that the proposed method can effectively improve the coherence between two complex SAR images and can validly detect human activity of interest.

show abstract

“…Our aim is to extend the CCD technique to detect disturbances of the ground in a forest. We first proposed the idea of using low frequency 3D SAR beamforming in [6] and presented further detail at the 2018 conference on radar [7]. In this work, we provide a more comprehensive analysis of the proposed solution.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the signal model and scene model are discussed in greater detail. To provide a complete, self‐contained analysis, elements in [7] are included and expanded as needed.…”

Section: Introductionmentioning

confidence: 99%

3D SAR coherent change detection for monitoring the ground under a forest canopy

Pincus

Preiss

2019

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

Coherent Change Detection Under a Forest Canopy

Cited by 4 publications

References 16 publications

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

A Coherence Improvement Method Based on Sub-Aperture InSAR for Human Activity Detection

3D SAR coherent change detection for monitoring the ground under a forest canopy

Contact Info

Product

Resources

About