Abstract
Analyzing land cover using remote sensing images has broad prospects, the precise segmentation of land cover is the key to the application of this technology. Nowadays, the Convolution Neural Network (CNN) is widely used in many image semantic segmentation tasks. However, existing CNN models often exhibit poor generalization ability and low segmentation accuracy when dealing with land cover segmentation tasks. To solve this problem, this paper proposes Dual Function Feature Aggregation Network (DFFAN). This method combines image context information, gathers image spatial information, and extracts and fuses features. DFFAN uses residual neural networks as backbone to obtain different dimensional feature information of remote sensing images through multiple downsamplings. This work designs Affinity Matrix Module (AMM) to obtain the context of each feature map and proposes Boundary Feature Fusion Module (BFF) to fuse the context information and spatial information of an image to determine the location distribution of each image’s category. Compared with existing methods, the proposed method is significantly improved in accuracy. Its mean intersection over union (MIoU) on the LandCover dataset reaches 84.81%.
Highlights
According to the study of Kussul et al [23] in processing land cover remote sensing images, deep learning algorithms are significantly better than machine learning algorithms such as the support vector machine (SVM)
Context Prior Layer and Aggregation Module in the CPNet increased the amount of calculation geometrically, so that as convolutional layers became deep, gradient would disappear, which affected the accuracy of semantic segmentation
In order to explore the effect of convolutional neural networks in land cover semantic segmentation, this paper proposed Dual Function Feature Aggregation Network (DFFAN) and conducted experiments on the LandCover
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. With the advancement of science and technology, we can obtain detailed land use information by analyzing remote sensing data. For example: Xu et al [4] employed a nonparametric rule-based classifier, which is based on decision tree learning They used decision tree regression to estimate the classification ratio of mixed pixels in remote sensing images and compared its classification. According to the study of Kussul et al [23] in processing land cover remote sensing images, deep learning algorithms are significantly better than machine learning algorithms such as the SVM. In view of the complex characteristics of the spectral environment in land cover segmentation, DFFAN aggregates contextual information and fuses spatial information of remote sensing images, improving the accuracy of semantic segmentation.
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