Targeted change detection in remote sensing images

Ignatiev, Vladimir; Trekin, Alexey; Lobachev, Viktor; Potapov, Georgy; Burnaev, Evgeny

doi:10.1117/12.2523141

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…However, their performance is limited when applied to HSIs due to their high dimensionality. Recently, several attempts had been introduced; these include tensor factorization, 3 orthogonal subspace mapping, multisource target feature support, 4 mixed pixel decomposition, 5 and independent component analysis. 6 In the literature, 2,7,8 CD is a composite workflow that contains a series of comprehensive processing steps: (1) problem understanding, (2) collection of appropriate data, (3) preprocessing, (4) relevant features selection, (5) design and implementation of CD algorithm, and (6) evaluation of CD performance.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral change detection based on modification of UNet neural networks

Moustafa

Mohamed

Ahmed

et al. 2021

J. Appl. Rem. Sens.

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The Earth's surface changes continuously due to several natural and humanmade factors. Efficient change detection (CD) is useful in monitoring and managing different situations. The recent rise in launched hyperspectral platforms provides a diversity of spectrum in addition to the spatial resolution required to meet recent civil applications requirements. Traditional multispectral CD algorithms hardly cope with the complex nature of hyperspectral images and their high dimensionality. To overcome these limitations, a CD deep convolutional neural network (CNN) semantic segmentation-based workflow was proposed. The proposed workflow is composed of four main stages, namely preprocessing, training, testing, and evaluation. Initially, preprocessing is performed to overcome hyperspectral image noise and the high dimensionality problem. Random oversampling (ROS), deep learning, and bagging ensemble were incorporated to handle imbalanced dataset. Also, we evaluated the generality and performance of the original UNet model and four variants of UNet, namely residual UNet, residual recurrent UNet, attention UNet, and attention residual recurrent UNet. Three hyperspectral CD datasets were employed in performance assessment for binary and multiclass change cases; all datasets suffer from class imbalance and small region of interest size. Recurrent residual UNet presented the best performance in both accuracy and inference time. Overall, the obtained results imply that deep CNN segmentation models can be utilized to implement efficient CD for hyperspectral imageries. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral change detection based on modification of UNet neural networks

Moustafa

Mohamed

Ahmed

et al. 2021

J. Appl. Rem. Sens.

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“…Semantic segmentation of remote sensing images is a critical process in the workflow of object-based image analysis, which aim is to assign each pixel to a semantic label [20,13]. It has applications in environmental monitoring, urban planning, forestry, agriculture, and other geospatial analysis.…”

Section: Introductionmentioning

confidence: 99%

Boundary Loss for Remote Sensing Imagery Semantic Segmentation

Bokhovkin

Burnaev

2019

Lecture Notes in Computer Science

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In response to the growing importance of geospatial data, its analysis including semantic segmentation becomes an increasingly popular task in computer vision today. Convolutional neural networks are powerful visual models that yield hierarchies of features and practitioners widely use them to process remote sensing data. When performing remote sensing image segmentation, multiple instances of one class with precisely defined boundaries are often the case, and it is crucial to extract those boundaries accurately. The accuracy of segments boundaries delineation influences the quality of the whole segmented areas explicitly. However, widely-used segmentation loss functions such as BCE, IoU loss or Dice loss do not penalize misalignment of boundaries sufficiently. In this paper, we propose a novel loss function, namely a differentiable surrogate of a metric accounting accuracy of boundary detection. We can use the loss function with any neural network for binary segmentation. We performed validation of our loss function with various modifications of UNet on a synthetic dataset, as well as using real-world data (ISPRS Potsdam, INRIA AIL). Trained with the proposed loss function, models outperform baseline methods in terms of IoU score.

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“…Obtaining such data in the context of remote sensing poses a significant challenge, as (1) aerial imagery data are expensive, (2) collection of raw data with satisfactory coverage and diversity is laborious, costly and error-prone, as is (3) manual image annotation; (4) moreover, in some instances such as change detection, the cost of collecting a representative number of rarely occurring cases can be prohibitively high. Unsurprisingly, despite public real-world annotated remote sensing datasets exist [48,8,27,19,38], these challenges have kept them limited in size, compared to general-purpose vision datasets such as the ImageNet [23].…”

Section: Introductionmentioning

confidence: 99%

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

Kolos

Marin

Artemov

et al. 2019

Lecture Notes in Computer Science

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Data-driven methods such as convolutional neural networks (CNNs) are known to deliver state-of-the-art performance on image recognition tasks when the training data are abundant. However, in some instances, such as change detection in remote sensing images, annotated data cannot be obtained in sufficient quantities. In this work, we propose a simple and efficient method for creating realistic targeted synthetic datasets in the remote sensing domain, leveraging the opportunities offered by game development engines. We provide a description of the pipeline for procedural geometry generation and rendering as well as an evaluation of the efficiency of produced datasets in a change detection scenario. Our evaluations demonstrate that our pipeline helps to improve the performance and convergence of deep learning models when the amount of real-world data is severely limited.

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Targeted change detection in remote sensing images

Cited by 11 publications

References 22 publications

Hyperspectral change detection based on modification of UNet neural networks

Hyperspectral change detection based on modification of UNet neural networks

Boundary Loss for Remote Sensing Imagery Semantic Segmentation

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

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