Design of Super Resolution and Fuzzy Deep Learning Architecture for the Classification of Land Cover and Landsliding Using Aerial Remote Sensing Data

Abstract

The diversity, noise, interimage interference, image distortion, and increase in the number of classes in aerial remotely sensed dataset cause exertion in the classification. The efficacy and stability of convolutional neural networks increase in image classification with the specified use of feature selection algorithm that causes remarkably improved decision making. To address the associated difficulties, a fuzzy deep learning architecture has been designed with a super-resolution technique that consists of 40 convolutional, four polling, four inverted bottleneck blocks, and one fully connected layer. The fuzzy optimistic formula is implemented in 4 blocks as an activation function where information is fused from the previous layers and present block while the rest are using the ReLU transfer function to handle the issue of noise and interimage interference. Feature selection is performed based on the physics of chaotic particle swarm optimization hybrid with the active set algorithm. The accuracy of the proposed architecture is examined on three diverse datasets: Bijie earth landslide/nonlandslide, EuroSAT, and NWPU-RESISC45, comprised of varying classes. The results are compared with state-of-the-art models, such as the hybrid version of VGGNet-16, Yolov4, ResNet-50, DenseNet-121, and other reported techniques. Moreover, the stability and computational complexity of the presented architecture are computed on 50 independent runs. It has been observed that the proposed architecture is stable, accurate, and viable and exploits a smaller number of learnable parameters than the models considered in comparison.

Keywords

• Aerial remote sensing (RS)
• chaotic particle swarm optimization (C-PSO)
• fuzzy-CNN deep learning (DL)
• Monte Carlo simulations
• statistical analysis and model stability