Fusing of Optical and Synthetic Aperture Radar (Sar) Remote Sensing Data: A Systematic Literature Review (Slr)

Mahyoub, S.; Fadil, Abdelhamid; Mansour, E. M.; Rhinane, Hassan; Al-Nahmi, F.

doi:10.5194/isprs-archives-xlii-4-w12-127-2019

Cited by 23 publications

(11 citation statements)

References 26 publications

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“…A vast number of studies have used the fusing of various sources of RS data for different aspects of land use and land cover classification [27,28]. In an analysis including 32 studies reviewing the advantages of fusing optical and radar data for land use analysis, the vast majority concluded that the fusion of RS data improved forest classification compared to using a single source [29].…”

Section: Introductionmentioning

confidence: 99%

Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data

Bjerreskov¹,

Nord‐Larsen

Fensholt

2021

Remote Sensing

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Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled the fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-sensor data will have a specific strength in the further classification of nemoral forest landscapes owing to the distinct seasonal patterns in the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and a random forest classifier trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90%, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34–74%. Species groups with coniferous species were the least confused, whereas the broadleaf groups, especially Quercus species, had higher error rates. The results are applied in Danish national accounting of greenhouse gas emissions from forests, resource assessment, and assessment of forest biodiversity potentials.

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Section: Introductionmentioning

confidence: 99%

Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data

Bjerreskov¹,

Nord‐Larsen

Fensholt

2021

Remote Sensing

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“…We can make a direct comparison with the stacking technique, a popular way of pixel-level fusing data from different sensors [20]. Direct stacking of bands can be used to derive ζ (without the Kronecker product).…”

Section: B Experiments -Ii: Dual-pol Sar -Multi-spectral Modalitymentioning

confidence: 99%

“…Multi-modal data includes, but is not limited to, dual-frequency polarimetric SAR (or PolSAR) or SAR-MS images from sensors with different spectral channels and resolutions. Various techniques for the fusion of SARoptical and subsequent CD algorithms have been reported in [20].…”

Section: Introductionmentioning

confidence: 99%

A General Framework for Change Detection Using Multimodal Remote Sensing Data

Chirakkal¹,

Bovolo²,

Misra³

et al. 2021

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

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A general framework for change detection (CD) is proposed to analyze multi-modal remotely sensed data utilizing the Kronecker product between two data representations (vectors or matrices). The proposed method is sensor independent and provides comparable results to techniques that exist for specific sensors. The proposed fusion technique is a pixel-level approach that incorporates inputs from different modalities, rendering enriched multi-modal data representation. Thus, the proposed hybridization procedure helps to assimilate multi-sensor information in a meaningful manner. A novel change index (ζ) is defined for the general multi-modal case. This index is then used to quantify the change in bi-temporal remotely sensed data. The article explores the usability, consistency, and robustness of the proposed multi-modal fusion framework, including the change index, with proper validation on two multi-modal cases:(1) the dual-frequency (C-and L-band) full-polarimetric Danish EMISAR data, and (2) the dual-polarimetric SAR and Sentinel-2 multi-spectral data. Detailed analysis and validation using extensive ground truth data are presented to establish the proposed framework.

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“…The three most commonly used Optical-Radar fusion methods at pixel level are Principal Component Analysis (PCA) [11], intensity-hue-saturation (IHS) [12], and discrete wavelet transform [13]. According to previous studies, PCA is the most preferred method of the three methods [14], while the Minimum Noise Fraction (MNF) transformation performs two separate standard PCA transformation of the noise-whitened data [15], and also aims to produce principal components by maximizing the signal-to-noise ratio of the data [16].…”

Section: Introductionmentioning

confidence: 99%

Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network

et al. 2020

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Annual crop inventory information is important for many agriculture applications and government statistics. The synergistic use of multi-temporal polarimetric synthetic aperture radar (SAR) and available multispectral remote sensing data can reduce the temporal gaps and provide the spectral and polarimetric information of the crops, which is effective for crop classification in areas with frequent cloud interference. The main objectives of this study are to develop a deep learning model to map agricultural areas using multi-temporal full polarimetric SAR and multi-spectral remote sensing data, and to evaluate the influence of different input features on the performance of deep learning methods in crop classification. In this study, a one-dimensional convolutional neural network (Conv1D) was proposed and tested on multi-temporal RADARSAT-2 and VENµS data for crop classification. Compared with the Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN) and non-deep learning methods including XGBoost, Random Forest (RF), and Support Vector Machina (SVM), the Conv1D performed the best when the multi-temporal RADARSAT-2 data (Pauli decomposition or coherency matrix) and VENµS multispectral data were fused by the Minimum Noise Fraction (MNF) transformation. The Pauli decomposition and coherency matrix gave similar overall accuracy (OA) for Conv1D when fused with the VENµS data by the MNF transformation (OA = 96.65 ± 1.03% and 96.72 ± 0.77%). The MNF transformation improved the OA and F-score for most classes when Conv1D was used. The results reveal that the coherency matrix has a great potential in crop classification and the MNF transformation of multi-temporal RADARSAT-2 and VENµS data can enhance the performance of Conv1D.

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Fusing of Optical and Synthetic Aperture Radar (Sar) Remote Sensing Data: A Systematic Literature Review (Slr)

Cited by 23 publications

References 26 publications

Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data

Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data

A General Framework for Change Detection Using Multimodal Remote Sensing Data

Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network

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