High-Resolution Urban Mapping by Fusion of Sar and Optical Data

Petrushevsky, Naomi; Manzoni, Marco; Guarnieri, Andrea Monti

doi:10.5194/isprs-archives-xliii-b3-2021-273-2021

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…The result shows that SVM yields the highest OA at 94% and Ka at 91%, respectively (Table 3). Similarly, different studies demonstrate the highest OA accuracy of classification by integration of optical and radar datasets [188,189].…”

Section: Discussionmentioning

confidence: 91%

Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia

Mengesha,

Desta,

Gamba

et al. 2024

Land

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In most developing countries, smallholder farms are the ultimate source of income and produce a significant portion of overall crop production for the major crops. Accurate crop distribution mapping and acreage estimation play a major role in optimizing crop production and resource allocation. In this study, we aim to develop a spatio–temporal, multi-spectral, and multi-polarimetric LULC mapping approach to assess crop distribution mapping and acreage estimation for the Oromia Region in Ethiopia. The study was conducted by integrating data from the optical and radar sensors of sentinel products. Supervised machine learning algorithms such as Support Vector Machine, Random Forest, Classification and Regression Trees, and Gradient Boost were used to classify the study area into five first-class common land use types (built-up, agriculture, vegetation, bare land, and water). Training and validation data were collected from ground and high-resolution images and split in a 70:30 ratio. The accuracy of the classification was evaluated using different metrics such as overall accuracy, kappa coefficient, figure of metric, and F-score. The results indicate that the SVM classifier demonstrates higher accuracy compared to other algorithms, with an overall accuracy for Sentinel-2-only data and the integration of optical with microwave data of 90% and 94% and a kappa value of 0.85 and 0.91, respectively. Accordingly, the integration of Sentinel-1 and Sentinel-2 data resulted in higher overall accuracy compared to the use of Sentinel-2 data alone. The findings demonstrate the remarkable potential of multi-source remotely sensed data in agricultural acreage estimation in small farm holdings. These preliminary findings highlight the potential of using multi-source active and passive remote sensing data for agricultural area mapping and acreage estimation.

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Section: Discussionmentioning

confidence: 91%

Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia

Mengesha,

Desta,

Gamba

et al. 2024

Land

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“…Good results are also achieved by Petrushevsky et al [72]. They explored the chance of combining the unique SAR ability to detect stable targets, e.g., urban structures, with the fine resolution of optical surveys.…”

Section: Type C-land Consumption Mapping Using Data Fusion or Data In...mentioning

confidence: 98%

Land Consumption Classification Using Sentinel 1 Data: A Systematic Review

Mastrorosa

Crespi

Congedo

et al. 2023

Land

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The development of remote sensing technology has redefined the approaches to the Earth’s surface monitoring. The Copernicus Programme promoted by the European Space Agency (ESA) and the European Union (EU), through the launch of the Synthetic Aperture Radar (SAR) Sentinel-1 and the multispectral Sentinel-2 satellites, has provided a valuable contribution to monitoring the Earth’s surface. There are several review articles on the land use/land cover (LULC) matter using Sentinel images, but it lacks a methodical and extensive review in the specific field of land consumption monitoring, concerning the application of SAR images, in particular Sentinel-1 images. In this paper, we explored the potential of Sentinel-1 images to estimate land consumption using mathematical modeling, focusing on innovative approaches. Therefore, this research was structured into three principal steps: (1) searching for appropriate studies, (2) collecting information required from each paper, and (3) discussing and comparing the accuracy of the existing methods to evaluate land consumption and their applied conditions using Sentinel-1 Images. Current research has demonstrated that Sentinel-1 data has the potential for land consumption monitoring around the world, as shown by most of the studies reviewed: the most promising approaches are presented and analyzed.

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High-Resolution Urban Mapping by Fusion of Sar and Optical Data

Cited by 2 publications

References 22 publications

Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia

Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia

Land Consumption Classification Using Sentinel 1 Data: A Systematic Review

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