Applications of Multi‐Source and Multi‐Sensor Data Fusion of Remote Sensing for Forest Species Mapping

Mohammadpour, Pegah; Viegas, Carlos

doi:10.1002/9781119788157.ch12

Cited by 6 publications

(2 citation statements)

References 75 publications

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“…It may be one of the reasons for misclassification in various wetland classes. Therefore, data from multi-sensors are preferred [48], and the combined use of multi-source satellite data can provide more wetland information [49]. In addition, in our present work, we evaluated the performances of the three feature selection methods based on the J-M distance using a single dataset, with regard to compression of feature number, selection of feature types, and classifier accuracy.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Comparison of the Applicability of J-M Distance Feature Selection Methods for Coastal Wetland Classification

Zhang

Lin

et al. 2023

Water

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Accurate determination of the spatial distribution of coastal wetlands is crucial for the management and conservation of ecosystems. Feature selection methods based on the Jeffries-Matusita (J-M) method include J-M distance with simple average ranking (JMave), J-M distance based on weights and correlations (JMimproved), and heuristic J-M distance (JMmc). However, as the impacts of these methods on wetland classification are different, their applicability has rarely been investigated. Based on the Google Earth Engine (GEE) and random forest (RF) classifier, this is a comparative analysis of the applicability of the JMave, JMimproved, and JMmc methods. The results show that the three methods compress feature dimensions and retain all feature types as much as possible. JMmc exhibits the most significant compression from a value of 35 to 15 (57.14%), which is 37.14% and 40% more compressed than JMave and JMimproved, respectively. Moreover, they produce comparable classification results, with an overall classification accuracy of 90.20 ± 0.19% and a Kappa coefficient of 88.80 ± 0.22%. However, different methods had their own advantages for the classification of different land classes. Specifically, JMave has a better classification only in cropland, while JMmc is advantageous for recognizing water bodies, tidal flats, and aquaculture. While JMimproved failed to retain vegetation and mangrove features, it enables a better depiction of the mangroves, salt pans, and vegetation classes. Both JMave and JMimproved rearrange features based on J-M distance, while JMmc places more emphasis on feature selection. As a result, there can be significant differences in feature subsets among these three methods. Therefore, the comparative analysis of these three methods further elucidates the importance of J-M distance in feature selection, demonstrating the significant potential of J-M distance-based feature selection methods in wetland classification.

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Section: Limitations and Future Workmentioning

confidence: 99%

Comparison of the Applicability of J-M Distance Feature Selection Methods for Coastal Wetland Classification

Zhang

Lin

et al. 2023

Water

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“…The design of multi-source meteorological satellites aims to overcome the limitations of single-satellite systems and enhance the monitoring capability of atmospheric, cloud, precipitation, and other meteorological phenomena on Earth [5]. This also results in TC remote sensing images presenting multi-source characteristics, such as visible light, infrared, water vapor, and microwave.…”

Section: Introduction 1motivation and Backgroundmentioning

confidence: 99%

Probabilistic Estimation of Tropical Cyclone Intensity Based on Multi-Source Satellite Remote Sensing Images

Song,

Yang,

et al. 2024

Remote Sensing

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Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the estimation results. However, TC, as a typical chaotic system, often requires confidence intervals for intensity estimates in real-world emergency decision-making scenarios. Additionally, the use of multi-source image inputs contributes to the uncertainty of the model. Consequently, this study introduces a neural network (MTCIE) that utilizes multi-source satellite images to provide probabilistic estimates of TC intensity. The model utilizes infrared and microwave images from multiple satellites as inputs. It uses a dual-branch self-attention encoder to extract TC image features and provides uncertainty estimates for TC intensity. Furthermore, a dataset for estimating the intensity of multi-source TC remote sensing images (MTCID) is constructed through the registration of latitude, longitude, and time, along with data augmentation. The proposed method achieves a MAE of 7.42 kt in deterministic estimation, comparable to mainstream networks like TCIENet. In uncertain estimation, it outperforms methods like MC Dropout in the PICP metric, providing reliable probability estimates. This supports TC disaster emergency decision making, enhancing risk mitigation in real-world applications.

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Introduction to Forest Monitoring Using Advanced Remote Sensing Technology – An Editorial Message

Pandey¹,

Arellano²

2022

Advances in Remote Sensing for Forest Monitoring

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Applications of Multi‐Source and Multi‐Sensor Data Fusion of Remote Sensing for Forest Species Mapping

Cited by 6 publications

References 75 publications

Comparison of the Applicability of J-M Distance Feature Selection Methods for Coastal Wetland Classification

Comparison of the Applicability of J-M Distance Feature Selection Methods for Coastal Wetland Classification

Probabilistic Estimation of Tropical Cyclone Intensity Based on Multi-Source Satellite Remote Sensing Images

Introduction to Forest Monitoring Using Advanced Remote Sensing Technology – An Editorial Message

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