Abstract
Detailed information regarding land utilization/cover is a valuable resource in various fields. In recent years, remote sensing images, especially aerial images, have become higher in resolution and larger span in time and space, and the phenomenon that the objects in an identical category may yield a different spectrum would lead to the fact that relying on spectral features only is often insufficient to accurately segment the target objects. In convolutional neural networks, down-sampling operations are usually used to extract abstract semantic features, which leads to loss of details and fuzzy edges. To solve these problems, the paper proposes a Multi-level Feature Aggregation Network (MFANet), which is improved in two aspects: deep feature extraction and up-sampling feature fusion. Firstly, the proposed Channel Feature Compression module extracts the deep features and filters the redundant channel information from the backbone to optimize the learned context. Secondly, the proposed Multi-level Feature Aggregation Upsample module nestedly uses the idea that high-level features provide guidance information for low-level features, which is of great significance for positioning the restoration of high-resolution remote sensing images. Finally, the proposed Channel Ladder Refinement module is used to refine the restored high-resolution feature maps. Experimental results show that the proposed method achieves state-of-the-art performance 86.45% mean IOU on LandCover dataset.
Highlights
Remote sensing images and image processing technology have been widely used in land description and change detection in urban and rural areas
In terms of up-sampling feature fusion, we propose another two modules, Multi-level Feature Attention Upsample (MFAU) and Channel Ladder Refinement (CLR)
3 indicates that using the Channel Feature Compression (CFC) module instead of the SE module increases the mean Intersection-over-Union (mIoU) by up to 0.36% but not increases the amounts of parameters and calculations. These results indicate that when the CFC module is regarded as the central block between the encoder and decoder, it can effectively extract the deep global features of high-resolution remote sensing images, optimize the learned context information, and alleviate the phenomenon of different spectra of the same object and the same spectrum of foreign objects to a certain extent
Summary
Remote sensing images and image processing technology have been widely used in land description and change detection in urban and rural areas. There were still some defects in existing land cover classification models. Among the traditional remote sensing image classification methods, the maximum likelihood (ML). Method [5] was widely used This method obtained the mean value and variance of each category through the statistics and calculation of the region of interest, thereby determining the corresponding classification functions. Other similar methods relied on the ability of trainers to perform spectral discrimination on the image feature space. With the development of remote sensing technology, image resolution continued to be improved, and spectral features were becoming more abundant, which
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