Author: CSDN @ _Yakult_
This article documents an example of building and training a Convolutional Neural Network (CNN) using Keras. This CNN model is used for classification tasks, and the training sample dataset comes from a CSV file. Our goal is to classify samples into different classes based on the input features.
This code can be applied in multiple directions in the field of remote sensing, some of the common application directions include:
Land cover classification : Remote sensing images are often used for land cover classification, which divides the land surface into different categories, such as forest, farmland, water, etc. Using 1D convolutional neural network to classify remote sensing data in CSV format can effectively extract information from multiple ground features and help achieve accurate land cover classification.
Vegetation monitoring based on time series data : Remote sensing images can be used for vegetation monitoring and change detection. Classification of time series remote sensing data through 1D convolutional neural network can identify the growth status and changes of vegetation, which is of great significance to agriculture, forestry and other fields.
Land use change analysis : 1D convolutional neural networks can be used to analyze land use changes in remote sensing images. By classifying historical remote sensing data, we can understand the trend of land use change and provide reference for urban planning and land resource management.
Water body monitoring : Remote sensing images can be used for water body monitoring, including identification and change detection of water bodies such as lakes, rivers, and reservoirs. The 1D convolutional neural network can capture the dynamic changes of water bodies from time-series remote sensing data, providing support for water resources management and environmental protection.
Climate Change Research : Remote sensing images provide a wide range of land surface information, which can be used to study climate change and environmental change. Classification and analysis of remote sensing data through 1D convolutional neural networks can reveal the impact of climate change on land surface characteristics.