Quarry recognition based on attention mechanism from high-resolution remote sensing imagery

doi:10.13474/j.cnki.11-2246.2021.346

Abstract

Abstract: Quarrying industry plays a positive rule for national economy. At the same time, it arouses hidden danger to ecology, environment, and human security. Currently, quarry ecological restoration has become an important issue for ecological civilization construction. The precondition to solve this problem is quarry recognition and boundary determination. High-resolution remote sensing earth observation and deep learning techniques provide an efficient way for this task. In this paper, we design an attention pyramid and a detail enhancement module based on residual network to recognize quarry in UAV high-resolution images, using only a small number of parameters and extremely low computational complexity. The experiments employ the UAV images and human annotated ground truths in Nanan city of Fujian province as the data source. Then, we construct a dataset for training convolutional neural networks, and validate the performance of our proposed method. The experimental results show that our proposed method is with high inference speed and accuracy, and can be used to provide supporting material to quarry ecological restoration.

Key words: quarry, high-resolution images, convolutional neural networks, attention mechanism, ecological restoration

CLC Number:

P237

MA Linfei, NI Huan, ZHOU Zihan. Quarry recognition based on attention mechanism from high-resolution remote sensing imagery[J]. Bulletin of Surveying and Mapping, 2021, 0(11): 96-100.

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