Classification of High-resolution Images with Local Binary Pattern and Convolutional Neural Network
Classification of High-resolution Images with Local Binary Pattern and Convolutional Neural Network: An Advanced Study
Abstract
It is very important to accurately classify high-resolution satellite images and to classify each section of the image separately. Complex patterns, on the other hand, are difficult to identify. The deep learning method is used to deal with this challenge. The goal of the deep learning method is to extract a large number of features without the need for human intervention. Nonetheless, integrating deep features with texture characteristics improves classification performance. Deep feature learning mixed with texture-based classification is made easier with the suggested system. Local Binary Pattern (LBP) is used to extract textural features, whereas Convolutional Neural Network is used to extract deep features (CNN). The main objectives of the proposed system are: (1) To efficiently combine deep features with texture features. (2) To increase the classification accuracy. (3) To classify the land cover/land map area of the remote sensing image correctly. The suggested method is implemented, and the results are checked to ensure its efficacy. When texture features are incorporated with a deep learning approach, experimental results demonstrate that classification performance has improved.
Layman Abstract
Classifying different areas in satellite images (like forests, water, or cities) is difficult, especially with complex patterns. This research combines two methods to do it better. First, a texture analyzer examines surface patterns. Second, an artificial intelligence (deep learning) automatically identifies features. Mixing these approaches improves accuracy, helping us correctly map land types from satellite photos without human help.
Keywords
Satellite imagery, remote sensing, image classification, deep learning, Convolutional Neural Network (CNN), texture analysis, Local Binary Pattern (LBP), feature extraction, land cover mapping
Hashtags
#SatelliteImagery, #DeepLearning, #RemoteSensing, #ImageClassification, #CNN, #ArtificialIntelligence, #MachineLearning, #LandCover, #Geospatial, #TextureAnalysis
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Source / Reference: https://doi.org/10.9734/bpi/naer/v3/1...