A neural network based classification of satellite images for change detection applications

Radhika, K.; Varadarajan, S.

doi:10.1080/23311916.2018.1484587

Cited by 17 publications

(7 citation statements)

References 11 publications

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“…Classification based methods includes post-classification comparison [23], Expectation Maximization (EM) algoirthms [24], unsupervised methods [25], and Artificial Neural Network (ANN) methods [23]. Classification based CD methods require a large high quality training images to obtain good accuracy of CD and they are unaffected by external factors such as atmospheric artefacts.…”

Section: A Change Detection Methodsmentioning

confidence: 99%

Detecting Urban Dynamics Using Deep Siamese Convolutional Neural Networks

Yekun,

Samsom

2020

Preprint

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Change detection is a fast growing descipline in the areas of computer vison and remote sensing. In this work, we designed and developed a variant of convolutional neural network (CNN), known as Siamese CNN to extract features from pairs of Sentinel-2 temporal images of Mekelle city captured at different times and detect changes due to urbanization: buildings and roads. The detection capability of the proposed was measured in terms of overall accuracy (95.8%), Kappa measure (72.5%), recall (76.5%), precision (77.7%), F1 measure (77.1%). The model has achieved a good performance in terms of most of these measures and can be used to detect changes in Mekelle and other cities at different time horizons undergoing urbanization.

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Section: A Change Detection Methodsmentioning

confidence: 99%

Detecting Urban Dynamics Using Deep Siamese Convolutional Neural Networks

Yekun,

Samsom

2020

Preprint

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“…Satellite devices offer cyclical images of surface coverage and help to obtain multi-temporal data sets for different use. Several measures for land cover change detection, including algebra based change detection approach (Ke et al, 2018;Ferraris et al, 2018), transform based change detection (Sadeghi et al, 2016;Massarelli 2018), classification based change detection (Radhika and Varadarajan 2018;Alonso et al, 2016), neural network and fuzzy based approach (Su et al, 2017;Zhang et al, 2017b;Tian and Gong 2018) can be implemented.…”

Section: Accuracy Assessment Of Classification and Land Cover Detecti...mentioning

confidence: 99%

Managing mangroves and coastal land cover in the Mekong Delta

Phan

Stive

2022

Ocean & Coastal Management

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“…The main advantage of these methods is that they provide accurate information on changes independent of external factors such as atmospheric disturbances. Radhika and Varadarajan proposed a classification detection method using neural networks that provides better accuracy but can only be applied to small images [35]. Another unsupervised novel SVD that traces the function clustering algorithm, which performs well in land-cover classification, was proposed by Vignesh et al The algorithm grouped images and used these images as a training set for the ensemble minimization learning algorithm (EML) [36].…”

Section: Change Detectionmentioning

confidence: 99%

A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery

Zhang

Liu

et al. 2022

Remote Sensing

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Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researchers’ attention. Most deep-learning-based methods only explore the spatial–spectral features simultaneously. However, we assume the key spatial-change areas should be more important, and attention should be paid to the specific bands which can best reflect the changes. To achieve this goal, we propose the spatial–spectral joint attention network (SJAN). Compared with traditional methods, SJAN introduces the spatial–spectral attention mechanism to better explore the key changed areas and the key separable bands. To be more specific, a novel spatial-attention module is designed to extract the spatially key regions first. Secondly, the spectral-attention module is developed to adaptively focus on the separable bands of land-cover materials. Finally, a novel objective function is proposed to help the model to measure the similarity of learned spatial–spectral features from both spectrum amplitude and angle perspectives. The proposed SJAN is validated on three benchmark datasets. Comprehensive experiments have been conducted to demonstrate the effectiveness of the proposed SJAN.

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A neural network based classification of satellite images for change detection applications

Cited by 17 publications

References 11 publications

Detecting Urban Dynamics Using Deep Siamese Convolutional Neural Networks

Detecting Urban Dynamics Using Deep Siamese Convolutional Neural Networks

Managing mangroves and coastal land cover in the Mekong Delta

A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery

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