Abstract
Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.
Highlights
(3) We explored the influence of imaging time intervals between optical images and synthetic aperture radar (SAR) images on glacial lake extraction under different fusion models
In the encoder–decoder semantic segmentation structure, the fusion of two modality data could occur at the input, encoder, decoder, or output, resulting in four fusion methdata could occur at the input, encoder, decoder, or output, resulting in four fusion methods: ods: (1) input fusion, in which two modality data are concatenated as the input data of a
In addition to the four fusion methods and atrous convolution fusion network (ACFNet) mentioned in Section 3, we trained and evaluated Wu’s model [46] and two other classical semantic segmentation models (SegNet and DeepLabV3+)
Summary
Glaciers have experienced extensive negative mass changes and greatly contributed to sea level rise [1]. Glacial lakes slightly alleviate sea level rise [2] by storing a small percentage of glacier meltwater. This small fraction of glacier meltwater has rapidly increased the size and number of glacial lakes over the last few decades [2,3,4]. Many studies on assessing GLOF hazards and risks have been published [6,7,9,10,11]. An inventory of glacial lakes is a prerequisite for most studies related to glacial lakes
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