Estimation of area and volume change in the glaciers of the Columbia Icefield, Canada using machine learning algorithms and Landsat images

Ambinakudige, Shrinidhi; Intsiful, Adjoa

doi:10.1016/j.rsase.2022.100732

Cited by 8 publications

(7 citation statements)

References 39 publications

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“…Yusof et al (2021) found that ANN yielded the worst results in their research for evaluating SVM, SAM, and ANN for classifying Landsat-8 and Sentinel-2 imageries (Dixit & Agarwal, 2020). Ambinakudige & Intsiful (2022) implemented SVM and ANN using hyperspectral data , they found that SVM producing superior outcomes to ANN (Alshari et al,2022).…”

Section: Random Forestmentioning

confidence: 99%

Assessment of Random Forest and Neural Network for Improving Land Use/ Land Cover Mapping from LIDAR Data and RGB Image: A Case Study of Magaga-El-Menia Governorate, Egypt

Taha,

Mandouh

2024

Geoplanning J. Geomatics Plann.

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The goals of this article are to improve classification of land use/land cover information using LIDAR data and RGB images, as well as to compare the performance of various supervised machine learning classifiers (random forest and neural network) for extracting land use/land cover information. The 3D coordinates are first transferred to a high-resolution raster via interpolation. Height and intensity raster grids are formed. Second, various raster maps - a normalized digital surface model (nDSM), the difference of returns, and the LiDAR intensity image -are combined to create a multi-channel image. Five scenarios with different combinations were created. Finally, on the five separate datasets, several classifications based on random forest and neural network classifiers were performed. The classification findings were subjected to a quantitative accuracy check. A comparison of these five methodologies has been conducted. Following that, morphological operations were used to eliminate noise. The results revealed also that the fourth approach is the best followed by the third approach then the last approach then the second approach followed by the first approach. It was discovered that random forest classification outperforms neural network classification in terms of classification accuracy.

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Section: Random Forestmentioning

confidence: 99%

Assessment of Random Forest and Neural Network for Improving Land Use/ Land Cover Mapping from LIDAR Data and RGB Image: A Case Study of Magaga-El-Menia Governorate, Egypt

Taha,

Mandouh

2024

Geoplanning J. Geomatics Plann.

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“…This study covered the literature review about ANN classifier from previous studies as follow: (Mishra et al, 2017;Kadavi and Lee, 2018;Dibs et al, 2020;Dixit and Agarwal, 2020;Ekumah et al, 2020;Hamad, 2020;Kaya and Görgün, 2020;MohanRajan et al, 2020;Navin and Agilandeeswari, 2020;Rojas et al, 2020;Saddique et al, 2020;Xu et al, 2020;Angessa et al, 2021;Bhattacharya et al, 2021;Dede et al, 2021;Ghayour et al, 2021;Sang et al, 2021;Xie et al, 2021;Yusof et al, 2021;Ambinakudige and Intsiful, 2022;Fantinel et al, 2022;Gogumalla et al, 2022;Rizvon and Jayakumar, 2022;Theres and Selvakumar, 2022).…”

Section: Ann Classifiermentioning

confidence: 99%

Classification of land use/land cover using artificial intelligence (ANN-RF)

Alshari

Abdulkareem

Gawali

2023

Front. Artif. Intell.

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Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).

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“…In the current study, however, ice was mappable without the use of thermal bands in the satellite imagery, by simple PBIA and by mean reflectance and object features in GEOBIA. In a machine learning-based classification of glacier surface classes, MXL, SVM, and RF classifiers were tested using a combination of Landsat TM and OLI with a range of normalized difference indices to support the characterization [81]. All three methods delivered greater than 99% accuracy, with the SVM only slightly outperforming the other methods.…”

Section: Manifestation Of Faciesmentioning

confidence: 99%

Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

Jawak¹,

Wankhede

Luis

et al. 2022

Remote Sensing

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Fundamental image processing methods, such as atmospheric corrections and pansharpening, influence the signal of the pixel. This morphs the spectral signature of target features causing a change in both the final spectra and the way different mapping methods may assign thematic classes. In the current study, we aim to identify the variations induced by popular image processing methods in the spectral reflectance and final thematic maps of facies. To this end, we have tested three different atmospheric corrections: (a) Quick Atmospheric Correction (QUAC), (b) Dark Object Subtraction (DOS), and (c) Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods: (a) Hyperspherical Color Sharpening (HCS) and (b) Gram–Schmidt (GS). WorldView-2 and WorldView-3 satellite images over Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard are tested via spectral subsets in traditional (BGRN1), unconventional (CYRN2), visible to near-infrared (VNIR), and the complete available spectrum (VNIR_SWIR). Thematic mapping was comparatively performed using 12 pixel-based (PBIA) algorithms and 3 object-based (GEOBIA) rule sets. Thus, we test the impact of varying image processing routines, effectiveness of specific spectral bands, utility of PBIA, and versatility of GEOBIA for mapping facies. Our findings suggest that the image processing routines exert an extreme impact on the end spectral reflectance. DOS delivers the most reliable performance (overall accuracy = 0.64) averaged across all processing schemes. GEOBIA delivers much higher accuracy when the QUAC correction is employed and if the image is enhanced by GS pansharpening (overall accuracy = 0.79). SWIR bands have not enhanced the classification results and VNIR band combination yields superior performance (overall accuracy = 0.59). The maximum likelihood classifier (PBIA) delivers consistent and reliable performance (overall accuracy = 0.61) across all processing schemes and can be used after DOS correction without pansharpening, as it deteriorates spectral information. GEOBIA appears to be robust against modulations in atmospheric corrections but is enhanced by pansharpening. When utilizing GEOBIA, we find that a combination of spatial and spectral object features (rule set 3) delivers the best performance (overall accuracy = 0.86), rather than relying only on spectral (rule set 1) or spatial (rule set 2) object features. The multiresolution segmentation parameters used here may be transferable to other very high resolution (VHR) VNIR mapping of facies as it yielded consistent objects across all processing schemes.

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Estimation of area and volume change in the glaciers of the Columbia Icefield, Canada using machine learning algorithms and Landsat images

Cited by 8 publications

References 39 publications

Assessment of Random Forest and Neural Network for Improving Land Use/ Land Cover Mapping from LIDAR Data and RGB Image: A Case Study of Magaga-El-Menia Governorate, Egypt

Assessment of Random Forest and Neural Network for Improving Land Use/ Land Cover Mapping from LIDAR Data and RGB Image: A Case Study of Magaga-El-Menia Governorate, Egypt

Classification of land use/land cover using artificial intelligence (ANN-RF)

Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

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