Adaptive enhanced interval type-2 possibilistic fuzzy local information clustering with dual-distance for land cover classification

Wu, Chengmao; Guo, Xiaokang

doi:10.1016/j.engappai.2022.105806

Cited by 7 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Saroj proposed a deep auto-encoder neural network architecture [14] with a focus on automating feature extraction and enhancing generalization capability; while this method is innovative in leveraging neighborhood rough sets, it do not fully capture the complex, multi-dimensional nature of remote sensing data, an area where our research contributes by implementing a more comprehensive feature analysis framework. Wu and Guo's introduction of a robust interval type-2 fuzzy clustering method [15] represents a significant stride in handling uncertainties in remote sensing image classification. Their approach to address category density and object spectra uncertainties is enlightening.…”

Section: Introductionmentioning

confidence: 99%

BayesNet: Enhancing UAV-Based Remote Sensing Scene Understanding with Quantifiable Uncertainties

Sagar,

Tanveer,

Chen

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Remote sensing stands as a fundamental technique in contemporary environmental monitoring, facilitating extensive data collection and offering invaluable insights into the dynamic nature of the Earth’s surface. The advent of deep learning, particularly convolutional neural networks (CNNs), has further revolutionized this domain by enhancing scene understanding. However, despite the advancements, traditional CNN methodologies face challenges such as overfitting in imbalanced datasets and a lack of precise uncertainty quantification, crucial for extracting meaningful insights and enhancing the precision of remote sensing techniques. Addressing these critical issues, this study introduces BayesNet, a Bayesian neural network (BNN)-driven CNN model designed to normalize and estimate uncertainties, particularly aleatoric and epistemic, in remote sensing datasets. BayesNet integrates a novel channel–spatial attention module to refine feature extraction processes in remote sensing imagery, thereby ensuring a robust analysis of complex scenes. BayesNet was trained on four widely recognized unmanned aerial vehicle (UAV)-based remote sensing datasets, UCM21, RSSCN7, AID, and NWPU, and demonstrated good performance, achieving accuracies of 99.99%, 97.30%, 97.57%, and 95.44%, respectively. Notably, it has showcased superior performance over existing models in the AID, NWPU, and UCM21 datasets, with enhancements of 0.03%, 0.54%, and 0.23%, respectively. This improvement is significant in the context of complex scene classification of remote sensing images, where even slight improvements mark substantial progress against complex and highly optimized benchmarks. Moreover, a self-prepared remote sensing testing dataset is also introduced to test BayesNet against unseen data, and it achieved an accuracy of 96.39%, which showcases the effectiveness of the BayesNet in scene classification tasks.

show abstract

Section: Introductionmentioning

confidence: 99%