A Multi-Dimensional Deep Siamese Network for Land Cover Change Detection in Bi-Temporal Hyperspectral Imagery

Seydi, Seyd Teymoor; Shah-Hosseini, Reza; Amani, Meisam

doi:10.3390/su141912597

Cited by 11 publications

(8 citation statements)

References 53 publications

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“…Moreover, the proposed method did not require the collection of user training data or the setting of parameters. Several HCD approaches have recently been presented to generate sample data using an unsupervised framework to enhance the sample data's reliability [10], [24]. These approaches primarily create sample data using classic predictors (e.g., PCA and EU predictors).…”

Section: Discussionmentioning

confidence: 99%

“…Compared to Multispectral Images (MSIs), HSI has highresolution spectral and spatial information with hundreds of bands, optimizing the quality of HRS images [4]. Since HSIs include a plethora of spectral information, they have been extensively used in various domains, including image classification [5], [6], anomaly detection [7], [8], change detection [9], [10], and so on.…”

Section: Introductionmentioning

confidence: 99%

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SSViT-HCD: A Spatial–Spectral Convolutional Vision Transformer for Hyperspectral Change Detection

Shafique

Seydi

Alipour-Fard

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent decades, the wide use of deep learningbased methods has consistently improved the performance of remote sensing images and is widely used for hyperspectral change detection (HCD) tasks. However, most of the existing HCD method is based on the convolutional neural network (CNN), which shows limitations in long-range dependencies and also cannot mine sequence features well. The CD performance still has margins for improvement. In this study, inspired by the excellent performance of transformers in computer vision and which has shown a significant ability to model global dependencies to attenuate the loss of long-range information, we built a hybrid spatial-spectral convolutional vision transformer (SSViT) for HCD. Our proposed method combines the merits of CNN and transformer to fulfill effective and efficient HCD. This study focused on highly reliable pseudo-sample data generation by selection scenario. To generate a pseudo sample, we have used different methods: (1) we predict change and no-change areas by using Euclidean distance, (2) thresholding by Chan-Vese segmentation method for determining change and no-change pixels for intensity maps, (3) sorting of change and no-change pixels, and (4) selection of the minimum value of initial no-change pixels as pseudo change sample data, in addition to, choosing the maximum intensity value for change candidate pixels as change sample data. The highly reliable change pixels were selected, and then pseudo-training data was used to train the SSViT model. At last, the change map is generated by training the SSViT network based on pseudo-training data. The performance of the SSViT model is evaluated for real-world hyperspectral (HS) datasets with different change landcover types. Furthermore, a new series of HS images is introduced for CD purposes. The results of CD show that the HS images have a high potential for detecting subtle changes. The experimental results demonstrate that the proposed SSViT could outperform the advanced HCD methods.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SSViT-HCD: A Spatial–Spectral Convolutional Vision Transformer for Hyperspectral Change Detection

Shafique

Seydi

Alipour-Fard

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…More details of each step are provided in the following subsections. Deep learning models have provided promising results in many applications [6,[40][41][42][43][44][45]. Although these methods can result in a high accuracy, they are more complicated compared to conventional machine learning algorithms and require a large amount of training datasets to produce accurate results [46].…”

Section: Methodsmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping

et al. 2022

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Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects. In this study, a new machine learning model based on the Cascade Forest Model (CFM) was developed for FSM. Satellite imagery, historical reports, and field data were used to determine flood-inundated areas. The database included 21 flood-conditioning factors obtained from different sources. The performance of the proposed CFM was evaluated over two study areas, and the results were compared with those of other six machine learning methods, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Deep Neural Network (DNN), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). The result showed CFM produced the highest accuracy compared to other models over both study areas. The Overall Accuracy (AC), Kappa Coefficient (KC), and Area Under the Receiver Operating Characteristic Curve (AUC) of the proposed model were more than 95%, 0.8, 0.95, respectively. Most of these models recognized the southwestern part of the Karun basin, northern and northwestern regions of the Gorganrud basin as susceptible areas.

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“…Each pixel of this image stores the intensity of change, and a binary thresholding algorithm is used to determine the intensity above which a pixel is defined as a change or not. Statistical techniques such as expectation-maximisation (EM) [34] or, most frequently, the Otsu algorithm [35][36][37][38] have been used to obtain an optimal threshold.…”

Section: Introductionmentioning

confidence: 99%

Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images

2023

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Change detection in very-high-spatial-resolution (VHR) remote sensing images is a very challenging area with applicability in many problems ranging from damage assessment to land management and environmental monitoring. In this study, we investigated the change detection problem associated with analysing the vegetation corresponding to crops and natural ecosystems over VHR multispectral and hyperspectral images obtained by sensors onboard drones or satellites. The challenge of applying change detection methods to these images is the similar spectral signatures of the vegetation elements in the image. To solve this issue, a consensus multi-scale binary change detection technique based on the extraction of object-based features was developed. With the objective of capturing changes at different granularity levels taking advantage of the high spatial resolution of the VHR images and, as the segmentation operation is not well defined, we propose to use several detectors based on different segmentation algorithms, each applied at different scales. As the changes in vegetation also present high variability depending on capture conditions such as illumination, the use of the CVA-SAM applied at the segment level instead of at the pixel level is also proposed. The results revealed the effectiveness of the proposed approach for identifying changes over land cover vegetation images with different types of changes and different spatial and spectral resolutions.

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A Multi-Dimensional Deep Siamese Network for Land Cover Change Detection in Bi-Temporal Hyperspectral Imagery

Cited by 11 publications

References 53 publications

SSViT-HCD: A Spatial–Spectral Convolutional Vision Transformer for Hyperspectral Change Detection

SSViT-HCD: A Spatial–Spectral Convolutional Vision Transformer for Hyperspectral Change Detection

Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping

Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images

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