Use of Multispectral and Hyperspectral Satellite Imagery for Monitoring Waterbodies and Wetlands

Hasanlou, Mahdi; Seydi, Seyd Teymoor

doi:10.1007/978-3-030-66238-7_9

Cited by 5 publications

(4 citation statements)

References 72 publications

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“…Advances in remote sensing (RS) technologies and evolution in smart sensors and charge-coupled device cameras providing higher resolution, wider coverage, lower cost, continuous information, and timely revisiting mean that earth observation and its regular monitoring become viable [15][16][17][18]. RS is deployed in many applications such as disaster mapping [19][20][21][22], environment monitoring [23,24], land Journal of Sensors use/cover mapping [25][26][27][28][29][30], and forest mapping [31,32]. Due to improvement of spatial and temporal resolution of satellite imagery and availability of synthetic aperture radar (SAR) dataset, disaster mapping based on RS data has been converted into a hot topic [33,34].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the big structure of datasets in the present study, the best solution for multisource dataset integration is feature-level fusion. DLbased methods provide promising results as a robust tool in RS and image processing communities [24,81]. The DL algorithm can automatically extract the deep features from the input data for flood mapping.…”

Section: Introductionmentioning

confidence: 99%

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Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

Seydi

Saeidi²,

Kalantar

et al. 2022

Journal of Sensors

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Floods, as one of the natural hazards, can affect the environment, damage the infrastructures, and threaten human lives. Due to climate change and anthropogenic activities, floods occur in high frequency all over the world. Therefore, mapping of the flood areas is of prime importance in disaster management. This research presents a novel framework for flood area mapping based on heterogeneous remote sensing (RS) datasets. The proposed framework fuses the synthetic aperture radar (SAR), optical, and altimetry datasets for mapping flood areas, and it is applied in three main steps: (1) preprocessing, (2) deep feature extraction based on multiscale residual kernel convolution and convolution neural network’s (CNN) parameter optimization by fusing the datasets, and (3) flood detection based on the trained model. This research exploits two large-scale area datasets for mapping the flooded areas in Golestan and Khuzestan provinces, Iran. The results show that the proposed methodology has a high performance in flood area detection. The visual and numerical analyses verify the effectiveness and ability of the proposed method to detect the flood areas with an overall accuracy (OA) higher than 98% in both study areas. Finally, the efficiency of the designed architecture was verified by hybrid-CNN and 3D-CNN methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

Seydi

Saeidi²,

Kalantar

et al. 2022

Journal of Sensors

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“…Multispectral images have numerous bands, ranging from visible to infrared light, and their extensive spectral information allows for reliable object identification. As a result, multispectral change detection has found widespread application in the fields of environmental monitoring [1][2][3][4], resource inquiry [5][6][7], urban planning [8][9][10], and natural catastrophe assessment [11][12][13].…”

Section: Introductionmentioning

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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“…Achieving this aim depends on certain determining factors, including the classification algorithm and input features. Deep-learning-based methods, representing one of the main subsets of machine learning, have recently been capable of yielding reliable results, and in turn have been used in many remote sensing applications, such as environment monitoring [19,20], change detection [21][22][23], target detection [24], and damage detection [25]. This study proposes a framework based on a deep learning method that is able to detect burned areas using high-resolution Sentinel-2 imagery.…”

Section: Introductionmentioning

confidence: 99%

Forest Burned Area Mapping Using Bi-Temporal Sentinel-2 Imagery Based on a Convolutional Neural Network: Case Study in Golestan Forest

Maskouni

Seydi

2021

The 8th International Electronic Conference on Sensors and Applications

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Forest areas are profoundly important to the planet, since they offer considerable advantages. The mapping and estimation of burned areas covered with trees are critical during decision making processes. In such cases, remote sensing can be of great help. This paper presents a method to estimate burned areas based on the Sentinel-2 imagery using a convolutional neural network (CNN) algorithm. The framework touches change detection using pre- and post-fire datasets. The proposed framework utilizes a multi-scale convolution block to extract deep features. We investigate the performance of the proposed method via visual and numerical analyses. The case study for this research is Golestan Forest, which is located in the north of Iran. The results of the burned area detection process show that the proposed method produces a performance accuracy rate of more than 97% in terms of overall accuracy, with a Kappa score greater than 0.933.

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Use of Multispectral and Hyperspectral Satellite Imagery for Monitoring Waterbodies and Wetlands

Cited by 5 publications

References 72 publications

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

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

Forest Burned Area Mapping Using Bi-Temporal Sentinel-2 Imagery Based on a Convolutional Neural Network: Case Study in Golestan Forest

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