Multi-Scale Context Aggregation for Semantic Segmentation of Remote Sensing Images

The semantic segmentation of remote sensing images (RSIs) is important in a variety of applications. Conventional encoder-decoder-based convolutional neural networks (CNNs) use cascade pooling operations to aggregate the semantic information, which results in a loss of localization accuracy and in t...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-02, Vol.12 (4), p.701
Main Authors: Zhang, Jing, Lin, Shaofu, Ding, Lei, Bruzzone, Lorenzo
Format: Article
Language:English
Subjects:
Accuracy
Agglomeration
Artificial neural networks
Coders
Cognition & reasoning
convolutional neural network
Datasets
Decoding
deep learning
Design
Embedding
Encoders-Decoders
Feature extraction
High resolution
image analysis
Image processing
Image segmentation
Localization
Neural networks
Preservation
Remote sensing
Semantic segmentation
Semantics
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Summary:The semantic segmentation of remote sensing images (RSIs) is important in a variety of applications. Conventional encoder-decoder-based convolutional neural networks (CNNs) use cascade pooling operations to aggregate the semantic information, which results in a loss of localization accuracy and in the preservation of spatial details. To overcome these limitations, we introduce the use of the high-resolution network (HRNet) to produce high-resolution features without the decoding stage. Moreover, we enhance the low-to-high features extracted from different branches separately to strengthen the embedding of scale-related contextual information. The low-resolution features contain more semantic information and have a small spatial size; thus, they are utilized to model the long-term spatial correlations. The high-resolution branches are enhanced by introducing an adaptive spatial pooling (ASP) module to aggregate more local contexts. By combining these context aggregation designs across different levels, the resulting architecture is capable of exploiting spatial context at both global and local levels. The experimental results obtained on two RSI datasets show that our approach significantly improves the accuracy with respect to the commonly used CNNs and achieves state-of-the-art performance.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12040701