A Tutorial and Performance Analysis on ENVI Tools for SAR Image Despeckling

Mohammad, R. Khosravi; Babak, Bahri-Aliabadi; Seyed, R. Salari; Samadi, Sadegh; Rostami, Habib; Karimi, Vahid

doi:10.2174/1574362413666181005101315

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…ENVI is a remote sensing image processing software developed by using the interactive data language (IDL). It allows for comprehensive and efficient extraction of relevant information from remote sensing images and provides a complete set of tools for processing and analyzing remote sensing data (Khosravi et al, 2020;Song, 2021). The remote sensing images are processed by using ENVI for tasks such as cropping, radiometric correction, and atmospheric correction.…”

Section: Data Sources and Methodsmentioning

confidence: 99%

Analysis of the evolution of the Yellow River Delta coastline and the response of the tidal current field

Zhang,

Shi,

Xing

et al. 2023

Front. Mar. Sci.

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The coastline of the Yellow River Delta has undergone continual alterations due to both natural forces and human activity. Studying these changes is crucial for promoting economic growth and preserving the region’s ecological balance. Based on imagery captured by the Landsat5 and Landsat8 satellites, this study uses statistical data from the Digital Shoreline Analysis System (DSAS), including Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR), to analyze the changes in the Yellow River Delta coastline from 2009 to 2019. This being the case, a hydrodynamic model under different shoreline conditions was established using Delft3D to compare and analyze the impact of shoreline changes on the tidal current field. From 2009 to 2019, the coastline in the study area exhibited an average movement distance of 1285m (NSM) and an annual change rate of 127.7m/a (LRR). The average increase in the area of the current estuary was 7.68km2/a, while the average decrease in the area of the old estuary was 4.91km2/a. Shoreline evolution is primarily influenced by the influx of water and sediment into the ocean. Following the implementation of water and sediment regulation, the existing estuary’s shoreline experienced a rapid initial accumulation of silt, which subsequently decelerated over time. Conversely, the former estuary has consistently undergone erosion. The greater the change in shoreline, the closer to the coast, and the greater the change in the tide. From 2009 to 2019, flow velocity and direction in the Yellow River Delta generally showed a decreasing trend, with changes ranging from 0.34% to 25.94%. The residual flow velocity near the current estuary gradually increased by about 2cm/s, while that near the abandoned old estuary gradually decreased by about 1cm/s, with no significant changes offshore. The sediment transported by the Yellow River is deposited at the current estuary, causing the coastline to move northeastward. In the abandoned estuary, there is erosion, causing the coastline to retreat.

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Section: Data Sources and Methodsmentioning

confidence: 99%

Analysis of the evolution of the Yellow River Delta coastline and the response of the tidal current field

Zhang,

Shi,

Xing

et al. 2023

Front. Mar. Sci.

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“…After orthorectification, geometric precision correction, atmospheric correction, fusion, and image mosaic, etc. [ 52 ], the spatial resolution reached 0.8 m, including four bands (R, G, B, NIR). According to the current status of urban land use, the labels are divided into seven types: impervious surface, building, vegetation, shadow, water, bare land, and background.…”

Section: Experiments Designmentioning

confidence: 99%

Multi-U-Net: Residual Module under Multisensory Field and Attention Mechanism Based Optimized U-Net for VHR Image Semantic Segmentation

Ran

Ding

Liu

et al. 2021

Sensors

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As the acquisition of very high resolution (VHR) images becomes easier, the complex characteristics of VHR images pose new challenges to traditional machine learning semantic segmentation methods. As an excellent convolutional neural network (CNN) structure, U-Net does not require manual intervention, and its high-precision features are widely used in image interpretation. However, as an end-to-end fully convolutional network, U-Net has not explored enough information from the full scale, and there is still room for improvement. In this study, we constructed an effective network module: residual module under a multisensory field (RMMF) to extract multiscale features of target and an attention mechanism to optimize feature information. RMMF uses parallel convolutional layers to learn features of different scales in the network and adds shortcut connections between stacked layers to construct residual blocks, combining low-level detailed information with high-level semantic information. RMMF is universal and extensible. The convolutional layer in the U-Net network is replaced with RMMF to improve the network structure. Additionally, the multiscale convolutional network was tested using RMMF on the Gaofen-2 data set and Potsdam data sets. Experiments show that compared to other technologies, this method has better performance in airborne and spaceborne images.

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“…KNN is an example-based algorithm with a wide range of applications [28][29][30][31]. The number of K in the fundamental structure of the KNN is required to determine the number of Ks nearest samples of a test.…”

Section: K Nearest Neighbormentioning

confidence: 99%

Fault detection and classification in smart grids using augmented K-NN algorithm

Hosseinzadeh

Masoodzadeh

Roshandel³

2019

SN Appl. Sci.

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The ability of artificial intelligence and machine learning techniques in classification and detection of the types of data in large datasets lead to their popularity among scientists and researchers. Because of the presence of different load at different times in power systems, it is hard to provide an accurate mathematical model for such systems. On the other hand, most of the available protection devices in power grids work based on the estimated mathematical models of the grid. For this reason, power system utilizers usually suffer from the low accuracy of the available protection systems in fault detection and diagnosis. In this paper, a reliable machine learning technique is proposed to detect and classify different faults of smart grids. The proposed technique benefits from the principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA is used to reduce the size of the dataset matrixes. The applied PCA reduces the dataset sizes and eliminates the possible singularity of the datasets. The LDA method is applied to the outputs data of the PCA to minimize the within class distance of the dataset and maximize the distance between classes. Finally, the well-known K-nearest neighbor technique is applied to detect the fault and determine its classes. The paper results demonstrate the effectiveness and robustness of the proposed algorithm in the determination of the fault class in smart grids.

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A Tutorial and Performance Analysis on ENVI Tools for SAR Image Despeckling

Cited by 11 publications

References 27 publications

Analysis of the evolution of the Yellow River Delta coastline and the response of the tidal current field

Analysis of the evolution of the Yellow River Delta coastline and the response of the tidal current field

Multi-U-Net: Residual Module under Multisensory Field and Attention Mechanism Based Optimized U-Net for VHR Image Semantic Segmentation

Fault detection and classification in smart grids using augmented K-NN algorithm

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