Qualitative and quantitative analysis of topographically derived CVA algorithms using MODIS and Landsat-8 data over Western Himalayas, India

Singh, Sartajvir; Sood, Vishakha; Taloor, Ajay Kumar; Prashar, Shivendu; Kaur, Ramneet

doi:10.1016/j.quaint.2020.04.048

Cited by 37 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From visual and statistical analysis, it has been observed that Hyperion EO-1 is capable of fetching precise information due to the large number of narrow bands which may use in the categorization of various class categories. Past literature states that extensive work has been done using multispectral datasets through numerous classifiers [89]. Despite various benefits, the multispectral dataset is not capable of producing the desired outcome because of a limited wider band.…”

Section: Resultsmentioning

confidence: 99%

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

et al. 2023

View full text Add to dashboard Cite

Monitoring the Earth’s surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth’s surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth’s surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring.

show abstract

Section: Resultsmentioning

confidence: 99%

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Over the past decade, extensive work has been done using multispectral datasets through numerous classifier or change detection models. 28,70 Despite various advantages, the multispectral dataset is not able to produce the desired results because of a minimal number of spectral bands. Many studies extended the applicability of remote sensing in the extraction of earth surface parameters using hyperspectral datasets over water bodies and wetlands, plant diseases, and forest monitoring.…”

Section: Discussionmentioning

confidence: 99%

“…In the past, many developments or improvements were made in the change detection procedures for different applications with different remote sensing datasets 1 , 27 . Some of the major improvements done in change detection models are changing vector analysis, 28 , 29 post-classification comparison (PCC), 30 – 32 subpixel-level, 33 , 34 and fusion-based 35 – 40 However, it has been observed that change detection via hyperspectral imaging delivers refined information in various narrow spectral bands that may not be possible with change detection via multispectral imaging.…”

Section: Introductionmentioning

confidence: 99%

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Dahiya

Gupta

Singh

2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Remote sensing is an effective way to analyze land surface changes on regular basis globally. In the previous literature, numerous change detection models were developed to detect the multitemporal or seasonal variations using different optical datasets, such as multispectral and hyperspectral. But there are many challenges involved in the processing of numerous bands, especially in the case of hyperspectral datasets, such as computational constraints and radiometric/atmospheric distortion. A simple framework-based artificial neural network (ANN) and postclassification comparison (PCC), named ANN-based PCC (ANPC), has been proposed to detect the multitemporal changes over agricultural land using hyperspectral, i.e., Earth Observation (EO-1) Hyperion and multispectral, i.e., Landsat-8. For cross-referencing, a comparative analysis is performed with respect to well-known PCC-based change detection methods, i.e., random forest-based post-comparison (RFPC) and support vector machine-based post-comparison (SVMPC). Experimental outcomes confirmed the effectiveness of ANPC (with an accuracy of more than 90%) in the extraction of multitemporal changes as compared to RFPC and SVMPC (with an accuracy of <90%). Our study enhances the utilization of the hyperspectral dataset (due to narrow spectral bands) in the extraction of critical information about the earth's surface parameters.

show abstract

“…The morphotectonic analyses is significant in under-standing the fluvial geomorphic processes, tectonic influence of land-scape and their role to drainage development (Horton, 1945;Schumm, 1956;Bull and McFadden, 1977;Keller and Pinter, 1996). Globally, the Digital Elevation data is used as an essential tool in GIS environment to analyze the morphotectonic characteristics of the drainage basins (Montgomery and Foufoula-Georgiou, 1993;Tucker, 1996;Hajam et al, 2013;Kothyari, 2014;Kothyari et al, 2016aKothyari et al, , 2017aKothyari et al, , 2016bWaikar and Nilawar, 2014;Sklar and Dietrich, 1998;Weissel and Seidl, 1998;Singh et al, 2017Singh et al, , 2020Sood et al, 2020). Conventionally, the drainage systems in an active tectonic terrain is impacted by river incisions, active folding and faulting and erodibility which leads to the formation of several alluvial landforms (Schumm, 1986;Keller and Pinter, 1996;Whipple et al, 2013;Kothyari, 2014;Kothyari et al, 2016aKothyari et al, , 2018aTaloor et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Tectonic imprints of landscape evolution in the Bhilangana and Mandakini basin, Garhwal Himalaya, India: A geospatial approach

Taloor

Joshi

Kotlia

et al. 2021

Quaternary International

Self Cite

View full text Add to dashboard Cite

Torrential rains, landslides, and seismic activity are the common factors that are causing unprecedented damage to life, property and infrastructure in Mandakini and Bhilangana basins of Garhwal Himalaya. Owing to such conditions, we demonstrate the feedback this landscape in Garhwal Himalaya in response to episodic tectonic uplift and monsoon precipitation. We calculated conventional geomorphic parameters to access the tectonic deformation across the major thrusts and faults. We further analyzed the normalized steepness index (Ksn), Chi (χ), and knickpoints along the longitudinal course of rivers. Additionally, the study attributed to active nature of Chail/Ramgarh thrust along the Balganga valley as envisaged by five levels of unpaired fluvial terraces, entrenched stream course, river ponding, active and stabilized landslide deposits, etc. Moreover, the flooding during the heavy rainfall events induced river toe cutting makes an effect on settlement over fluvial deposits. Therefore, we suggested that the highly dissected and tilted basins with deep V shaped valleys and ongoing seismicity also fabricates the region more vulnerable for hazards which threaten the human lives.

show abstract

Qualitative and quantitative analysis of topographically derived CVA algorithms using MODIS and Landsat-8 data over Western Himalayas, India

Cited by 37 publications

References 37 publications

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

Qualitative and quantitative analysis of artificial neural network-based post-classification comparison to detect the earth surface variations using hyperspectral and multispectral datasets

Tectonic imprints of landscape evolution in the Bhilangana and Mandakini basin, Garhwal Himalaya, India: A geospatial approach

Contact Info

Product

Resources

About