Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks

Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches.

Research on Classification of Remote Sensing Images Based on Artificial Intelligence

With the rapid development of image processing technology, remote sensing technology has received increasing attention. Relying on artificial intelligence technology and using the advantages of principal component analysis (PCA) to reduce the dimensionality of features, this paper proposes a remote sensing image classification method based on SVM. First, LBP operator is used to extract remote sensing image features, and then PCA is used to perform remote sensing image features. The dimensionality reduction process reduces the feature dimensionality and eliminates feature redundant information, and obtains features that have a large contribution to the classification result. Finally, SVM is used for remote sensing image classification. The results show that PCA-SVM improves the efficiency and accuracy of remote sensing image classification.