A novel change detection framework based on deep learning for the analysis of multi-temporal polarimetric SAR images

De, Shaunak; Pirrone, Davide; Bovolo, Francesca; Bruzzone, Lorenzo; Bhattacharya, Avik

doi:10.1109/igarss.2017.8128171

Cited by 28 publications

(19 citation statements)

References 8 publications

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“…Their three key problems include: (1) suppressing speckle noise; (2) designing a change metric or a change indicator; and (3) using a threshold or a classifier based on a change metric to generate a final change map. Change detection methods using AI techniques, especially an autoencoder (AE) [97][98][99][100][101][102][103][104][105][106][107] and a convolutional neural network (CNN) [108][109][110][111][112][113][114], to suppress speckle noise and extract features has been proven to be the state of the art. In the overall process and framework of methods, they are similar to the methods based on optical RS images, and the detailed framework and AI model introduction are analyzed in Sections 4 and 5.…”

Section: Sar Imagesmentioning

confidence: 99%

“…Although unsupervised change detection does not require labeled training samples, sometimes the lack of prior knowledge makes it unsuitable for change detection involving semantic information. Weakly and semi-supervised schemes use inaccurate or insufficient labeled samples as a priori knowledge to solve this problem, which can be implemented with label aggregation [97], iterative learning [58,185], deep generative models [186] (see Section 5.6 for a more detailed review), sample generation strategies [156], or novel cost functions [36,187].…”

Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

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Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

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Section: Sar Imagesmentioning

confidence: 99%

Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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“…Next, some feature maps extracted by the SAE are taken as the training data for the CNN. In addition, an autoencoder and multi-layer perceptron (MLP) are combined to identify changed pixels [31]. Change detection using faster R-CNN has been proposed for high-resolution images [32].…”

Section: Deep Convolutional Network and Related Studies On Change Detmentioning

confidence: 99%

“…Sci. 2019, 9, x FOR PEER REVIEW 4 of 16 changed pixels [31]. Change detection using faster R-CNN has been proposed for high-resolution images [32].…”

Section: Deep Convolutional Network and Related Studies On Change Detmentioning

confidence: 99%

Fusion Network for Change Detection of High-Resolution Panchromatic Imagery

Wiratama

Sim

2019

Applied Sciences

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This paper proposes a fusion network for detecting changes between two high-resolution panchromatic images. The proposed fusion network consists of front-and back-end neural network architectures to generate dual outputs for change detection. Two networks for change detection were applied to handle image-and high-level changes of information, respectively. The fusion network employs single-path and dual-path networks to accomplish low-level and high-level differential detection, respectively. Based on two dual outputs, a two-stage decision algorithm was proposed to efficiently yield the final change detection results. The dual outputs were incorporated into the two-stage decision by operating logical operations. The proposed algorithm was designed to incorporate not only dual network outputs but also neighboring information. In this paper, a new fused loss function was presented to estimate the errors and optimize the proposed network during the learning stage. Based on our experimental evaluation, the proposed method yields a better detection performance than conventional neural network algorithms, with an average area under the curve of 0.9709, percentage correct classification of 99%, and Kappa of 75 for many test datasets.

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“…For example, Gao et al [20] presented a registration algorithm of multi-temporal images based on image geometric structural and gray information. Shaunak proposed a novel deep learning based weakly-supervised framework for urban change detection using multi-temporal polarimetric synthetic aperture radar (SAR) data in [21]. Pablo [22] introduced an automatic image orientation method with multi-temporal images, which is helpful for the use of multi-temporal images in the procedure of block adjustment.…”

Section: Introductionmentioning

confidence: 99%

Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery Based on Statistical Learning Theory

Jiao

Wang

You

et al. 2018

Sensors

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With the increasing demand for high-resolution remote sensing images for mapping and monitoring the Earth’s environment, geometric positioning accuracy improvement plays a significant role in the image preprocessing step. Based on the statistical learning theory, we propose a new method to improve the geometric positioning accuracy without ground control points (GCPs). Multi-temporal images from the ZY-3 satellite are tested and the bias-compensated rational function model (RFM) is applied as the block adjustment model in our experiment. An easy and stable weight strategy and the fast iterative shrinkage-thresholding (FIST) algorithm which is widely used in the field of compressive sensing are improved and utilized to define the normal equation matrix and solve it. Then, the residual errors after traditional block adjustment are acquired and tested with the newly proposed inherent error compensation model based on statistical learning theory. The final results indicate that the geometric positioning accuracy of ZY-3 satellite imagery can be improved greatly with our proposed method.

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A novel change detection framework based on deep learning for the analysis of multi-temporal polarimetric SAR images

Cited by 28 publications

References 8 publications

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Fusion Network for Change Detection of High-Resolution Panchromatic Imagery

Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery Based on Statistical Learning Theory

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