Adaptive multi-level feature learning framework for natural disaster image classification

Gourav Mondal

doi:10.1007/s12145-026-02202-x

Adaptive multi-level feature learning framework for natural disaster image classification

2026-08-10

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Gourav Mondal

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Rajesh Kumar Dhanaraj *

DOI：10.1007/s12145-026-02202-x

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Natural disaster recognition is crucial in disaster management, emergency response, and damage assessment. In modern scenarios, even leveraging Deep Learning (DL), specifically CNNs and hybrid learning models, delivers significant solutions, there are still many challenges in the field, such as redundant feature extraction, high computational cost, and decreased reliability in noisy and heterogeneous environments. These constraints may affect their utility in disaster contexts, where rapid verdict-creation is acute. To tackle such challenges, this work proposes a novel multi-level feature-learning framework that incorporates adaptive image enhancement, hybrid segmentation, multi-texture feature extraction, attention-based classification, and a self-adaptive optimization strategy within a single architecture for image classification of natural disasters. The proposed system combines the Adaptive Long Recursive Kalman Filter (ALRKF) and a Bi-LSTM (Bidirectional Long Short-Term Memory) network to reduce noise and enhance images across different environments adaptively. The hybrid U-ResSegNet architecture combines the best of both architectures, U-Net and ResNet-50, to achieve better segmentation results, enabling the extraction of regions without losing fine spatial details or high-level semantic information. The segmented images are then described using Local Binary Pattern (LBP) to capture complementary local texture characteristics, and Local Ternary Pattern (LTP) and GLCM (Grey-Level Co-occurrence Matrix) to capture complementary global texture characteristics. A feature classification-based attentive mechanism network is leveraged to classify the resulting feature representations, and the parameters are optimized using the proposed Self-Adaptive Driving Training Optimization (SA-DTO) algorithm, which enhances the convergence stability and learning efficiency. The Multi-Class Disaster Images Dataset and the Disaster Images Dataset (CNN-Model) tested the proposed framework, achieving classification accuracies of 98.97% and 99.16% in a complex, noisy environment.

Keywords:

Disaster classification

Adaptive long recursive Kalman filter

U-ResSegNet

Dual-MLP

And Self-adaptive driving training optimization algorithm

Adaptive multi-level feature learning framework for natural disaster image classification

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