Deforestation rate estimation using crossbreed multilayer convolutional neural networks

Deforestation is an important environmental issue that involves the removal of forests on a large scale, resulting in ecological imbalance and biodiversity loss. Synthetic Aperture Radar (SAR) images are widely used as a valuable tool to detect deforestation effectively. The SAR technology allows ca...

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Published in:Multimedia tools and applications Vol. 83; no. 33; pp. 79453 - 79479
Main Authors: Subhahan, D. Abdus, Kumar, C. N. S. Vinoth
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2024
Springer Nature B.V
Subjects:
Artificial neural networks
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Deforestation
Freight cars
Image resolution
Machine learning
Mapping
Multilayers
Multimedia Information Systems
Neural networks
Pattern recognition
Radar imaging
Remote monitoring
Special Purpose and Application-Based Systems
Synthetic aperture radar
Weather
Deforestation
Crossbreed multilayer CNN
Box car mean squared sparse coding filter
Synthetic aperture radar
ISSN:1573-7721, 1380-7501, 1573-7721
Online Access:Get full text
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Summary:Deforestation is an important environmental issue that involves the removal of forests on a large scale, resulting in ecological imbalance and biodiversity loss. Synthetic Aperture Radar (SAR) images are widely used as a valuable tool to detect deforestation effectively. The SAR technology allows capturing high-resolution images irrespective of weather conditions or daylight, making it helpful to monitor remote and densely vegetated areas. Recently, deep learning techniques used on SAR images have showcased promising results in the automation of deforestation detection and mapping processes. By leveraging neural networks (NNs) and machine learning (ML) systems, these approaches examine SAR data to recognize deforestation patterns and estimate deforestation rates over time. Therefore, this study develops a cross-breed multilayer convolutional neural network (CNN) for deforestation rate estimation in the Amazon. The proposed model initially preprocesses the input SAR data to remove the speckle noise using a box car mean squared sparse coding filter (BCMSSCF). Besides, crossbreed multilayer CNN (CM_CNN) is used for mapping and segmentation of the deforested area. To determine the pace of deforestation in the Amazon region, a widespread experimental analysis was performed on the LBA-ECO LC-14 dataset. A detailed comparative result analysis of the proposed model is made with recent approaches. The experimental results stated that the proposed model shows promising results in terms of different performance measures.
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-024-19319-0