A Hybrid CNN + Random Forest Approach to Delineate Debris Covered Glaciers Using Deep Features

Nijhawan, Rahul; Das, Josodhir; Raman, Balasubramanian

doi:10.1007/s12524-018-0750-x

Cited by 32 publications

(19 citation statements)

References 28 publications

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“…optical remote sensing data, topographic data, temperature data, and InSAR coherence data). The most promising results (96% and 91% accuracy) were reported by Nijhawan, Das, and Balasubramanian (2018), Robson et al (2015) using novel hybrid deep learning framework approach and OBIA. However, except (Robson et al 2015) none of them could achieve higher accuracy over large geographical regions.…”

Section: Existing Approaches and Emerging Area Of Researchmentioning

confidence: 95%

“…Applicability of artificial neural network (ANN) classifier for estimation of debris over Himalayan glaciers is reported by several studies (Bishop, Shroder Jr, and Hickman 1999;Garg et al 2017;Nijhawan, Das, and Balasubramanian 2018). Bishop, Shroder, and Hickman.…”

Section: Ann and Cnn Approachmentioning

confidence: 99%

“…More recently, deep learning has become popular among earth scientists and is applied to solve numerous outstanding research problems, including glacier mapping and characterization (Castelluccio et al 2015). A study reported by Nijhawan, Das, and Balasubramanian (2018) demonstrates the novel hybrid deep learning framework approach for delineation of supra-glacial debris in parts of Alaknanda basin, Uttarakhand. Their presented approach utilizes multi-phase deep learning and random forest framework by the ensemble of CNN's (CNN-A, CNN-B, and CNN-C).…”

Section: Ann and Cnn Approachmentioning

confidence: 99%

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Development of glacier mapping in Indian Himalaya: a review of approaches

Kaushik

Joshi

Singh

2019

International Journal of Remote Sensing

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The paper reviews the status of glacier mapping with special reference to the Indian Himalaya. The review provides information on various satellite remote sensing data interpretation methods used with special emphasis laid on recent semi-automated algorithms used for glacier and debris-cover mapping, along with their limitations and challenges. Further, the pragmatic solutions on offer are discussed, and the emerging areas of glacier mapping research are highlighted. The review also touchescontribution of Survey of India (SOI) and Geological Survey of India (GSI) in the glacier mapping. Finally, it discusses the wider range of spatial and spectral domains in which remote sensing data helps to inventories glaciers. The review also identifies gaps in using the latest techniques like Unmanned Aerial Vehicles (UAVs) and machine learning algorithms to improvise on the ongoing efforts. At last, the review provides an exhaustive list of references on glacier mapping from the Indian Himalaya as benefit to readers.

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Section: Existing Approaches and Emerging Area Of Researchmentioning

confidence: 95%

Section: Ann and Cnn Approachmentioning

confidence: 99%

Section: Ann and Cnn Approachmentioning

confidence: 99%

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Development of glacier mapping in Indian Himalaya: a review of approaches

Kaushik

Joshi

Singh

2019

International Journal of Remote Sensing

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“…The CNN algorithm has outstanding performance in terms of data local feature extraction. Nijhawan et al [40] used CNN integration to realize the automatic mapping of the border of the debris area and achieved good results. Marochov et al [37] used a CNN model to automatically obtain pixel-level glacier information.…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [55] used the CNN model to extract the surface temperature, combining it with other features of the SAR image, and effectively determined glacier information using the RF algorithm. Therefore, the respective advantages of RF and CNN [40] can be leveraged to enhance the classification results through decision fusion strategies. This demonstrates the potential for improving the final classification performance, so as to build a more reasonable RF-CNN composite classifier for glacier classification.…”

Section: Introductionmentioning

confidence: 99%

Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers

Zhang

Shangguan

et al. 2021

Remote Sensing

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Glaciers in High Mountain Asia (HMA) have a significant impact on human activity. Thus, a detailed and up-to-date inventory of glaciers is crucial, along with monitoring them regularly. The identification of debris-covered glaciers is a fundamental and yet challenging component of research into glacier change and water resources, but it is limited by spectral similarities with surrounding bedrock, snow-affected areas, and mountain-shadowed areas, along with issues related to manual discrimination. Therefore, to use fewer human, material, and financial resources, it is necessary to develop better methods to determine the boundaries of debris-covered glaciers. This study focused on debris-covered glacier mapping using a combination of related technologies such as random forest (RF) and convolutional neural network (CNN) models. The models were tested on Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), selecting Eastern Pamir and Nyainqentanglha as typical glacier areas on the Tibetan Plateau to construct a glacier classification system. The performances of different classifiers were compared, the different classifier construction strategies were optimized, and multiple single-classifier outputs were obtained with slight differences. Using the relationship between the surface area covered by debris and the machine learning model parameters, it was found that the debris coverage directly determined the performance of the machine learning model and mitigated the issues affecting the detection of active and inactive debris-covered glaciers. Various classification models were integrated to ascertain the best model for the classification of glaciers.

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Rock glaciers as proxy for machine learning based debris‐covered glacier mapping of Kinnaur District, Himachal Pradesh

Pradhan,

Mahanta,

Tiwari

et al. 2024

Earth Surf Processes Landf

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This research introduces an innovative approach by utilising rock glaciers (RGs) as a proxy for mapping debris‐covered glaciers (DCGs). This approach focuses on the interconnected nature of glaciers, DCGs and RGs in a continuum where DCGs can transform into RGs over time due to various processes. This study utilises six machine learning models—logistic regression (LR), support vector machine (SVM), K‐nearest neighbour (KNN), Naïve Bayes (NB), decision tree (DT) and random forest (RF)—combined with multispectral satellite data (Sentinel‐2 and Landsat 8) and topographical data derived from ALOS PALSAR DEM. Performance metrics such as accuracy, area under the curve (AUC) score, precision, recall and F1‐score were evaluated to assess model performance. This detailed mapping provides a precise estimation of the extent of DCGs in the Kinnaur district. The estimated DCG areas revealed intriguing variation across models, with RF (9.71%), KNN (9.67%) and NB (9.41%) yielding similar predictions. SVM (11.61%) projected a slightly larger DCG area, whereas DT (5.54%) and LR (25.55%) provided contrasting results. Validation against high‐resolution satellite images, Google Earth images and glacier inventories confirmed the accuracy and reliability of our approach. Based on our findings for our specific study, the most effective method for mapping DCGs is RF, followed by KNN, NB, DT and SVM. The combination of machine learning models and RG data presents a novel and promising approach to remote sensing‐based DCG mapping, with potential applications for other regions and broader environmental studies.

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A Hybrid CNN + Random Forest Approach to Delineate Debris Covered Glaciers Using Deep Features

Cited by 32 publications

References 28 publications

Development of glacier mapping in Indian Himalaya: a review of approaches

Development of glacier mapping in Indian Himalaya: a review of approaches

Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers

Rock glaciers as proxy for machine learning based debris‐covered glacier mapping of Kinnaur District, Himachal Pradesh

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