Abstract
Water conservancy remote sensing image classification is an important task for water conservancy image interpretation, which provides indispensable analysis results for the applications of water conservancy remote sensing images. However, in the high-resolution remote sensing images of water conservancy, the objects and water bodies are usually diverse, and the image semantics are fuzzy, leading to poor classification performance. To handle it, this paper proposes a novel classification method based on target-scene deep semantic enhancement for high-resolution remote sensing images of water conservancy, which consists of two key branches. The upper branch is an improved ResNet18 network based on dilated convolution, which is used to extract scene-level features of images. The lower branch is a novel multi-level semantic-understanding based Faster R-CNN, which is used to extract the target-level features of images. The experimental results show that the features extracted by the proposed method contain more discriminative scene-level information as well as more detailed target-level information, which can effectively help generate better classification performance.
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Wang, X., Zuo, G., Li, K., Li, L., Shi, A. (2023). Water Conservancy Remote Sensing Image Classification Based on Target-Scene Deep Semantic Enhancement. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14256. Springer, Cham. https://doi.org/10.1007/978-3-031-44213-1_20
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