Aerial image change detection using dual regions of interest networks

Han, Pengcheng; Ma, Cunbao; Li, Qing; Leng, Pengyu; Bu, Shuhui; Li, Ké

doi:10.1016/j.neucom.2019.04.029

Cited by 30 publications

(10 citation statements)

References 33 publications

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“…Specifically, the most commonly used types of multispectral images for AI-based change detection methods are derived from the Landsat series of satellites and the Sentinel series of satellites [66,67], due to their low acquisition cost and high time and space coverage. In addition, other satellites, such as Quickbird [68][69][70][71][72][73][74], SPOT series [75][76][77][78], Gaofen series [14,79,80], Worldview series [81][82][83][84][85], provide high and very high spatial resolution images, and various aircrafts provide very high spatial resolution aerial images [20,[86][87][88][89][90][91][92][93][94], allowing the change detection results to retain more details of the changes. HSIs have hundreds or even thousands of continuous and narrow bands, which can provide abundant spectral and spatial information.…”

Section: Optical Rs Imagesmentioning

confidence: 99%

“…Change detection is a spatiotemporal analysis and can be achieved by acquiring the spatial-spectral features through an AI-based feature extractor as a spectral-spatial module, and then modeling temporal dependency through an AI-based classifier as a temporal module [14,38,53,74]. Moreover, this hybrid structure is skillfully used for unsupervised change detection [100] and object-level change detection [87]. This makes the whole change detection process more complicated while improving performance.…”

Section: Multi-model Integrated Structurementioning

confidence: 99%

“…However, to achieve special functions, the CNNs integrate some special structures for change detection. For instance, the region CNN (R-CNN), primitively designed for object detection in CV, contains a region-proposals structure to predict the regions of the changed objects [87,196,197]. The PCANet, with its convolution filter banks chosen from PCA filters, is able to reduce the influence of speckle noise and has been used in SAR image change detection [178,180].…”

Section: Convolutional Neural Networkmentioning

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: Optical Rs Imagesmentioning

confidence: 99%

Section: Multi-model Integrated Structurementioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

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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“…Addressing CD as a semantic segmentation problem, the models directly obtained a change map that match input images dimension. Examples of the models include, dual regions of interest networks [29], U-Net based CD [30], FCNN [31], Siamese-FCNN [32], improved U-Net++ architecture [29],CDNet [31], Pyramid-FCNN [33], Faster Mask R-CNN [34], and stacked Contractive autoencoder (sCAE) [15].…”

Section: A Change Detectionmentioning

confidence: 99%

Training Compact Change Detection Network for Remote Sensing Imagery

et al. 2021

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Change Detection (CD) is a hot remote sensing topic where the change zones are highlighted by analyzing bi-temporal or multi-temporal images. Recently, Deep learning (DL) paved the road to implement various reliable change detection approaches that overcome traditional CD methods limitation. However, high performance DL based approaches have explosion number of parameters that demanded extensive computation and memory usage in addition to large volumes of training data. To address this issue, we proposed a teacher-student setting for remote sensing imagery change detection. To distill the knowledge from the over-parameterized Siamese teacher network, we proposed tiny student network that was trained using the obtained categorical distribution of probability from the teacher paired Softmax output at high temperature. Practical Swarm Optimization (PSO) was applied in order to optimally configure student architecture. Finally, ample experiments were conducted on LEVIR-CD dataset. Also, we introduced EGSAR-CD dataset, which contains of a large set of bi-temporal SAR images with 460 image pairs (256×256). Experiment results indicate that we can reach up to 5.4× reduction rate in number of parameters with loss of accuracy between 5% and 6% on the LEVIR-CD and EGSAR-CD datasets utilizing selfknowledge distillation.

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“…14: Representative GAN based semi-supervised methods [73], [164] designed for change detection preserving the detailed low-level features which contributes to more accurate pixel-wise binary segmentation at the final layer. Therefore several methods [74], [80], [84], [85], [87], [89]- [91], [136], [138], [139], [153], [154], [157] have used skip and residual connections in the network to obtain better performance (refer Fig. 9b, Fig.…”

Section: D-cnnmentioning

confidence: 99%

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Mandal¹,

Vipparthi²

2021

Preprint

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Visual change detection, aiming at segmentation of video frames into foreground and background regions, is one of the elementary tasks in computer vision and video analytics. The applications of change detection include anomaly detection, object tracking, traffic monitoring, human machine interaction, behavior analysis, action recognition, and visual surveillance. Some of the challenges in change detection include background fluctuations, illumination variation, weather changes, intermittent object motion, shadow, fast/slow object motion, camera motion, heterogeneous object shapes and realtime processing. Traditionally, this problem has been solved using hand-crafted features and background modelling techniques. In recent years, deep learning frameworks have been successfully adopted for robust change detection. This article aims to provide an empirical review of the state-of-the-art deep learning methods for change detection. More specifically, we present a detailed analysis of the technical characteristics of different model designs and experimental frameworks. We provide model design based categorization of the existing approaches, including the 2D-CNN, 3D-CNN, ConvLSTM, multi-scale features, residual connections, autoencoders and GAN based methods. Moreover, an empirical analysis of the evaluation settings adopted by the existing deep learning methods is presented. To the best of our knowledge, this is a first attempt to comparatively analyze the different evaluation frameworks used in the existing deep change detection methods. Finally, we point out the research needs, future directions and draw our own conclusions.

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Aerial image change detection using dual regions of interest networks

Cited by 30 publications

References 33 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

Training Compact Change Detection Network for Remote Sensing Imagery

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

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