A Multisquint Framework for Change Detection in High-Resolution Multitemporal SAR Images

Domínguez, Elías Méndez; Meier, Erich; Small, David; Schaepman, Michael E.; Bruzzone, Lorenzo; Henke, Daniel

doi:10.1109/tgrs.2018.2805471

Cited by 23 publications

(20 citation statements)

References 38 publications

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“…A hierarchical approach to change detection developed for surveillance applications is found in [8]. In [9], a multisquint framework, which is similar to beamforming, is proposed to mitigate false alarms caused by image artifacts typically found in high-resolution SAR imagery and urban scenarios. Recently, research on using classification methods, e.g., machine learning, in SAR change detection has been of interest [10]- [12].…”

Section: A Motivation and Related Workmentioning

confidence: 99%

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New Forms of Likelihood Ratio Test for SAR Change Detection

Pettersson

Sjogren

2021

IEEE Access

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The Neyman-Pearson lemma, i.e., the likelihood ratio test and its generalized version, have been used for the development of the synthetic aperture radar (SAR) change detection methods. For detecting changes caused by targets on the ground such as vehicles, a target model, or at least certain assumptions concerning the targets, are always required for deriving a statistical hypothesis test. Without the prior knowledge on targets, it is difficult to make any assumption. An inappropriate assumption can degrade change detection performance significantly. To avoid this technical issue, the new forms of likelihood ratio test for SAR change detection are introduced in this paper. The proposed forms are shown to be very flexible. They can be utilized to develop change detection methods for different types of data, e.g., data in scalar form, data in vector form, data represented in complex number, and data represented in real number. The flexibility of the proposed forms is also shown by the the capability to implement change detection methods in the iterative and non-iterative ways. For the illustration purpose, a new change detection method is developed on one of the introduced forms and tested using TanDEM-X data measured in Karlshamn, Sweden in 2016.

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Section: A Motivation and Related Workmentioning

confidence: 99%

“…Two forms of likelihood ratio test (8) and (9) show themselves two different processing approaches. A simple processing scheme can be used for the test (9), whereas (8) requires a more comprehensive processing scheme. We start here with the simple one.…”

Section: B Processing Schemementioning

confidence: 99%

New Forms of Likelihood Ratio Test for SAR Change Detection

Pettersson

Sjogren

2021

IEEE Access

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“…Based on the D-S theory, Wu [171] has indicated to incorporate original images with the generated edge-derived line-density-based visual saliency (LDVS) feature and the texture-derived built-up presence index (PanTex) feature. In addition, regardless of time efficiency, it turns out that the fusion of multiple similarity features, such as grey level co-occurrence matrix, Gaussian Markov random field, and Gabor features, can also add additional spatial information and improve the accuracy of change detection [172]. • Fusion of extracted results: Multi-temporal RS images can be disassembled into several sub-data, which emphasizes abundant object characteristics and spatial behaviors of changing targets, such as shapes and distance of the surrounding environment.…”

Section: Fusion Of Characteristics Of Geographic Entity and Rs Imagesmentioning

confidence: 99%

“…Recognizing the diversity of data utilization, it is possible to fuse sub-results extracted from sub-data. For example, separately analyzing the combinations of certain bands in multispectral images [46] or fusing the results extracted from the multi-squint views filtered from the poly-directional pulses of SAR images with the single-look change result [172]. Similarly, not only the results of diverse data, but the results of the multiple change detection methods are also desirable to be fused [173].…”

mentioning

confidence: 99%

“…Determining which sets of pixels need to be associated is the first step in the association. In addition to the fix-size window [172], the adaptive regions obtained by adaptive clustering of spectral correlation [107] or density correlation [185], and iterative neighborhoods around the high confidence changed or unchanged pixels [77] are feasible basic processing units. Extracting the correlation of pixels within the unit is another challenge.…”

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confidence: 99%

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A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020

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Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research.

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