Speckle filtering impact on land use/land cover classification area using the combination of Sentinel-1A and Sentinel-2B (a case study of Kirkuk city, Iraq)

Hasan, Sumaya Falih; Shareef, Muntadher Aidi; Hassan, Nihad Davut

doi:10.1007/s12517-021-06494-9

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Additional pre-processing steps like Speckle Filtering were done as shown in Figure 6 to potentially reduce the inherent speckle effects and improve the accuracy of classifying land covers (Hasan, 2021). Terrain correction was also done to remove distortions inherent to image acquisition and apply appropriate map coordinates.…”

Section: Novasar Pre-processing Workflowmentioning

confidence: 99%

Analysis of Novasar-1 S-Band Data in Developing an Alternative Land Cover Mapping

Mariano,

Dumalag,

Cadiz

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The Advanced Science and Technology Institute of the Department of Science and Technology (DOST-ASTI), through its Synthetic Aperture Radar and Automatic Identification System (SARwAIS) Project, has gained access to S-Band SAR images acquired by the NovaSAR-1 satellite of UK’s Surrey Satellite Technology Ltd. (SSTL) To help maximize the utility of these images especially in the aspect of terrain-related applications, their viability as potential alternatives to datasets like optical satellite images and other SAR images in characterizing land cover types was evaluated. Statistical analyses on the backscatter values from the tri-polarization ScanSAR datasets using Multivariate Analysis of Variance (MANOVA) and its corresponding post-hoc tests showed that there is a significant difference on the mean backscatter values at 0.05 level of significance. Moreover, Tukey’s honestly significant difference (Tukey’s HSD) test determined which pairs contribute to the significant difference. Using the Random Forest algorithm resulted in an accuracy of 66.93% without further optimization and/or reduction in the number of land covers being classified. Despite the relatively unremarkable accuracy score, it still showed potential for data augmentation with optical satellites for land cover mapping and other terrain-related applications.

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Section: Novasar Pre-processing Workflowmentioning

confidence: 99%

Analysis of Novasar-1 S-Band Data in Developing an Alternative Land Cover Mapping

Mariano,

Dumalag,

Cadiz

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Afterwards, a speckle filtering step was applied to all Sentinel-1 scenes. To this end, a mono-temporal improved Lee sigma despeckling algorithm with a kernel size of 5 × 5 was implemented to reduce the undesirable speckle effect, enhancing pixel-based classification results [73,74]. Finally, all the preprocessed Sentinel-1 scenes were categorized based on acquisition seasons, and a mean reducer function was applied to aggregate Sentinel-1 images and to create seasonal SAR features.…”

Section: Satellite Images Preprocessingmentioning

confidence: 99%

Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery

Ghorbanian

Ahmadi

Amani³

et al. 2022

Water

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Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital for their conservation and sustainable blue carbon development. In this regard, the joint use of remote sensing data and machine learning algorithms can assist in producing accurate mangrove ecosystem maps. This study investigated the potential of artificial neural networks (ANNs) with different topologies and specifications for mangrove classification in Iran. To this end, multi-temporal synthetic aperture radar (SAR) and multi-spectral remote sensing data from Sentinel-1 and Sentinel-2 were processed in the Google Earth Engine (GEE) cloud computing platform. Afterward, the ANN topologies and specifications considering the number of layers and neurons, learning algorithm, type of activation function, and learning rate were examined for mangrove ecosystem mapping. The results indicated that an ANN model with four hidden layers, 36 neurons in each layer, adaptive moment estimation (Adam) learning algorithm, rectified linear unit (Relu) activation function, and the learning rate of 0.001 produced the most accurate mangrove ecosystem map (F-score = 0.97). Further analysis revealed that although ANN models were subjected to accuracy decline when a limited number of training samples were used, they still resulted in satisfactory results. Additionally, it was observed that ANN models had a high resistance when training samples included wrong labels, and only the ANN model with the Adam learning algorithm produced an accurate mangrove ecosystem map when no data standardization was performed. Moreover, further investigations showed the higher potential of multi-temporal and multi-source remote sensing data compared to single-source and mono-temporal (e.g., single season) for accurate mangrove ecosystem mapping. Overall, the high potential of the proposed method, along with utilizing open-access satellite images and big-geo data processing platforms (i.e., GEE, Google Colab, and scikit-learn), made the proposed approach efficient and applicable over other study areas for all interested users.

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“…Moreover, numerous researches aimed to evaluate the impact of speckle on LULC classification accuracy applied on fused radar or optical satellite images (Hasan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Object-Based Approaches for Land Use-Land Cover Classification Using High Resolution Quick Bird Satellite Imagery (A Case Study: Kerbela, Iraq)

Jaber

Shareef

Merzah

2022

Geodesy and Cartography

Self Cite

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Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that use of textural data during the object image classification approach can considerably enhance land use classification performance. Moreover, the results showed higher overall accuracy (86.02%) in the o object based method than pixel based (79.06%) in urban extractions. The object based performed much more capabilities than pixel based.

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Speckle filtering impact on land use/land cover classification area using the combination of Sentinel-1A and Sentinel-2B (a case study of Kirkuk city, Iraq)

Cited by 8 publications

References 35 publications

Analysis of Novasar-1 S-Band Data in Developing an Alternative Land Cover Mapping

Analysis of Novasar-1 S-Band Data in Developing an Alternative Land Cover Mapping

Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery

Object-Based Approaches for Land Use-Land Cover Classification Using High Resolution Quick Bird Satellite Imagery (A Case Study: Kerbela, Iraq)

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