Abstract
Aiming at the problems of high-resolution remote sensing images with many features and low classification accuracy using a single feature description, a remote sensing image land classification model based on deep learning from the perspective of ecological resource utilization is proposed. Firstly, the remote sensing image obtained by Gaofen-1 satellite is preprocessed, including multispectral data and panchromatic data. Then, the color, texture, shape, and local features are extracted from the image data, and the feature-level image fusion method is used to associate these features to realize the fusion of remote sensing image features. Finally, the fused image features are input into the trained depth belief network (DBN) for processing, and the land type is obtained by the Softmax classifier. Based on the Keras and TensorFlow platform, the experimental analysis of the proposed model shows that it can clearly classify all land types, and the overall accuracy, F1 value, and reasoning time of the classification results are 97.86%, 87.25%, and 128 ms, respectively, which are better than other comparative models.
Highlights
Remote sensing technology is a technology of observing ground objects by detecting remote sensing images through different working platforms and processing remote sensing information to obtain some dynamic information, so as to obtain ground information [1]
(4) e test data are input into depth belief network (DBN) for testing using the same feature fusion method, and the Softmax method is used to complete the classification of remote sensing image land types
A remote sensing image land classification model based on deep learning from the perspective of ecological resource utilization is proposed, combining feature-level image fusion methods and DBN network model processing and analysis of remote sensing image data obtained by the Gaofen-1 satellite to obtain land types efficiently and accurately
Summary
Remote sensing technology is a technology of observing ground objects by detecting remote sensing images through different working platforms and processing remote sensing information to obtain some dynamic information, so as to obtain ground information [1]. Remote sensing image classification is of great significance for obtaining image information It has a wide range of applications in national defense and security construction, urban planning, disaster monitoring, land use, landscape analysis, agricultural remote sensing, etc. Remote sensing image acquisition technology has developed rapidly, and the acquired images have become more and more abundant. Such as hyperspectral images and high-resolution images that contain richer feature information [7]. (2) In order to improve the accuracy of remote sensing image land classification, the proposed model uses DBN to process the fused image features. Combining the results of forward unsupervised classification and label data, the training network is fine-tuned according to the law of error back propagation, shortening the time of land classification
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