A Theoretical Framework for Change Detection Based on a Compound Multiclass Statistical Model of the Difference Image

Zanetti, Massimo; Bruzzone, Lorenzo

doi:10.1109/tgrs.2017.2759663

Cited by 46 publications

(27 citation statements)

References 28 publications

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“…In [26], alternative approaches are mentioned. Statistical methods such as [39,60] have been used in CVA analyses. Experimental results showed that the change detection results can almost approach optimal performance using some satellite images.…”

Section: Other Approachesmentioning

confidence: 99%

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

Information

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Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner’s viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted.

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Section: Other Approachesmentioning

confidence: 99%

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

Information

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“…-multi-temporal: is one of the most investigated forms of fusion in remote sensing due to the rich information content hidden in the temporal dimension. In particular, it can be applied to strictly time-related tasks, like prediction [13], change detection [27][28][29] and co-registration [30], and general-purpose tasks, like segmentation [7], despeckling [31] and feature extraction [32][33][34], which do not necessarily need a joint processing of the temporal sequence, but can benefit from it. -multi-sensor: is gaining an ever growing importance due both to the recent deployment of many new satellites and to the increasing tendency of the community to share data.…”

Section: Introductionmentioning

confidence: 99%

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

et al. 2018

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Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account-optical sequences, SAR sequences, digital elevation model-so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May-November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators.

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“…Two main steps are usually related to these methods, i.e., the generation of a change magnitude image (CMI) and the use of a binary threshold to divide the CMI into a binary change detention map (BCDM). The most commonly used methods to provide CMI are image difference [2,29,30], image ratios [31,32] and change vector analysis (CVA) [33][34][35][36]. In general, these methods usually calculate the distance between the bi-temporal images pixel by pixel to measure the change magnitude between the bi-temporal images.…”

Section: Introductionmentioning

confidence: 99%

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

Liu

Zhang

et al. 2018

Remote Sensing

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Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy.

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A Theoretical Framework for Change Detection Based on a Compound Multiclass Statistical Model of the Difference Image

Cited by 46 publications

References 28 publications

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

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