Multilevel and Multi-granularity of Remote Sensing Imagery Application based on Deep Learning and Machine Learning Algorithm (original) (raw)

2021 The 4th International Conference on Machine Learning and Machine Intelligence

Abstract

Deep learning classification has state-of-the-art machine learning approaches. Earlier work proves the deep convolutional neural network was successful and brilliant in different tasks such as image classification and image processing in remote sensing datasets. To recognize and clarify the physical aspect of the earth's surface land cover and exploit the land use is an exciting issue in environmental monitoring analyzing remote sensing data that is free and easy to get in different areas without time-consuming. To improve the quality of data sources and cooperating with image representation is still lack methods. We started with the traditional segmentation approach to divide an image into a single and compare multi-level into multiple segmentation for remote sensing imagery. We review the traditional machine learning and deep learning neural network classification tasks. We collected a dataset from six countries of Eastern Africa Communities with nine categories. We proposed an ensemble average model neural network with three models, combined as a single model trained, and achieved 96.5% validation accuracy. Finally, we compared state-of-the-art and extracted features pre-trained weight ImageNet and used traditional machine learning algorithms to improve accuracy.

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