A self-paced learning algorithm for change detection in synthetic aperture radar images

Shang, Ronghua; Yuan, Yijing; Jiao, Licheng; Yue, Meng; Ghalamzan, Amir Masoud

doi:10.1016/j.sigpro.2017.07.023

Cited by 32 publications

(24 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The validity and robustness of models are reflected through the detection accuracy of the changed area, the accuracy of the non-changed area, and the overall detection indexes (overall accuracy (OA), kappa, AUC, and F1). [12,94,151,157,159], respectively. The internal parameter settings are mentioned in the original literatures.…”

Section: Discussionmentioning

confidence: 99%

“…Obviously, the otherness of the output demands becomes one of the challenges for urban change detection technology. Meanwhile, multi-source satellite sensors provide a variety of RS images for change detection, such as synthetic aperture radar (SAR) [11,12], multispectral [13], and hyperspectral images [14,15]. In fact, the characteristics of different RS images are distinctive, e.g., speckle noise of SAR images, multiple bands of multispectral images, and mixed pixel of hyperspectral images.…”

Section: Motivation and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

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

2020

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Motivation and Problem Statementmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

show abstract

“…Other change detection methods, such as the principal component analysis (PCA) [12], the Gram-Schmidt transformation [13] and the scale-invariant feature transformation (SIFT) [14], mapped the original image into the feature space to label the changed areas. The evolution of artificial intelligence has resulted in the development of deep-learning-based change detection methods for multitemporal remote sensing images: the self-paced learning method [15], the principal component analysis network (PCANET) [16], the convolutional wavelet neural network (CWNN) [17], etc. The pixels in the difference image were pre-classified and used to train the network.…”

Section: Introductionmentioning

confidence: 99%

Change Detection in Multitemporal Monitoring Images Under Low Illumination

et al. 2020

View full text Add to dashboard Cite

Video surveillance may involve the simultaneous monitoring of a large number of areas. Realtime automatic change detection of a monitoring area (such as involving the movement of people or vehicles) can reduce risks incurred in negligent manual observation. However, the low signal-to-noise ratio (SNR) of dark environments can significantly corrupt camera images, making it difficult for machine learning surveillance systems to detect small changes in monitored images. In addition, in the absence of changes between two multitemporal monitoring images, sensor noise can lead to false alarms. The objective of this paper is to reduce the effect of sensor noise on change detection of monitored images and the run time of change detection algorithms. For these purposes, we proposed a novel multitemporal monitoring image change detection algorithm based on morphological structure filtering and normalized fusion difference image. First, the random noise in two surveillance images was removed using a multidirectional weighted multiscale series of a morphological filter. Next, two difference images were obtained by using the compression log-ratio operator and the mean ratio operator, and a fusion difference image was obtained by equal-weight fusion of the two difference images. Then, the sigmoid function was used to compress the fusion difference map to obtain a normalized fusion difference image, and a median filter was used to obtain a final difference image. Finally, the k-means clustering algorithm was utilized to obtain the change detection results. The experimental results demonstrate that the proposed method can accurately detect changes in a night monitoring scene in real time. Subjective and objective evaluation of the experimental results demonstrate that the proposed method is superior to reference algorithms in terms of change detection accuracy, time and robustness. INDEX TERMS change detection; morphological structure filtering; normalized fusion difference map; low illumination monitoring image

show abstract

“…SPL has been widely used in many problems such as multimedia search [30], long-term tracking [31] and visual category discovery [32]. In the field of SAR images processing, Shang et al [33] proposed an algorithm based on SPL for change detection in SAR images.…”

Section: Introductionmentioning

confidence: 99%

Self-Paced Convolutional Neural Network for PolSAR Images Classification

et al. 2019

View full text Add to dashboard Cite

Fully polarimetric synthetic aperture radar (PolSAR) can transmit and receive electromagnetic energy on four polarization channels (HH, HV, VH, VV). The data acquired from four channels have both similarities and complementarities. Utilizing the information between the four channels can considerably improve the performance of PolSAR image classification. Convolutional neural network can be used to extract the channel-spatial features of PolSAR images. Self-paced learning has been demonstrated to be instrumental in enhancing the learning robustness of convolutional neural network. In this paper, a novel classification method for PolSAR images using self-paced convolutional neural network (SPCNN) is proposed. In our method, each pixel is denoted by a 3-dimensional tensor block formed by its scattering intensity values on four channels, Pauli’s RGB values and its neighborhood information. Then, we train SPCNN to extract the channel-spatial features and obtain the classification results. Inspired by self-paced learning, SPCNN learns the easier samples first and gradually involves more difficult samples into the training process. This learning mechanism can make network converge to better values. The proposed method achieved state-of-the-art performances on four real PolSAR dataset.

show abstract

A self-paced learning algorithm for change detection in synthetic aperture radar images

Cited by 32 publications

References 40 publications

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

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

Change Detection in Multitemporal Monitoring Images Under Low Illumination

Self-Paced Convolutional Neural Network for PolSAR Images Classification

Contact Info

Product

Resources

About