Abstract
Semantic segmentation of high-resolution aerial images is a concerning issue of remote sensing applications. To address the issues of intra-class heterogeneity and inter-class homogeneity, a novel end-to-end semantic segmentation network, namely Context and Semantic Enhanced High-Resolution Network (CSE-HRNet), is proposed in this paper. Two procedures are considered comprehensively, which are multi-scale contextual feature extractor and multi-level semantic feature producer. Nested Dilated Residual Block (NDRB) is designed firstly, which could enhance the representational power of multi-scale contexts and tackle the issue of intra-class heterogeneity. The pyramidal feature hierarchy is introduced secondly, by which multi-level feature fusions could be utilized to enlarge inter-class semantic differences. Experimental results verify that, based on the Potsdam and Vaihingen benchmarks, the proposed CSE-HRNet can achieve competitive performance compared with other state-of-the-art methods.
Highlights
W ITH the rapid development of aerial remote sensing technologies, the increased accessibility to highresolution aerial images has opened up new horizons in the remote sensing community for various application fields, such as traffic monitoring [1], urban planning [2], intelligent agriculture [3], and disaster management [4]
The proposed method was evaluated with two public benchmarks, i.e., the Potsdam dataset and the Vaihingen dataset, both of which are provided by International Society for Photogrammetry and Remote Sensing (ISPRS). These two datasets contain the high-resolution true ortho photo (TOP), the digital surface model (DSM), and the normalized DSM data, with the corresponding ground truth labels. While both DSM and nDSM data are included in the two datasets, we only focused on the raw TOP images in this work, following [40], [43], [46]
WORK In this paper, a notable CSE-High-Resolution Network (HRNet) mode is presented for the semantic segmentation of high-resolution aerial images
Summary
W ITH the rapid development of aerial remote sensing technologies, the increased accessibility to highresolution aerial images has opened up new horizons in the remote sensing community for various application fields, such as traffic monitoring [1], urban planning [2], intelligent agriculture [3], and disaster management [4]. Towards the automated interpretation of aerial images, semantic segmentation (i.e., semantic labeling) is a crucial step to extract valuable information from the regions of interest, inferring every pixel in an image with the information about categories of the ground objects. Aerial images acquired with high spatial resolutions are expecting to exhibit a great diversity of objects, and provide very detailed geometric information. Objects of the different categories having the same colors or interacted with cast shadows would present very similar visual characteristics. These confusing objects lead to the issues of intra-class heterogeneity and inter-class homogeneity, both of which pose extreme challenges for accurate and coherent segmentation [5]–[7]
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