Deep-Separation Guided Progressive Reconstruction Network for Semantic Segmentation of Remote Sensing Images

Abstract

The success of deep learning and the segmentation of remote sensing images (RSIs) has improved semantic segmentation in recent years. However, existing RSI segmentation methods have two inherent problems: (1) detecting objects of various scales in RSIs of complex scenes is challenging, and (2) feature reconstruction for accurate segmentation is difficult. To solve these problems, we propose a deep-separation-guided progressive reconstruction network that achieves accurate RSI segmentation. First, we design a decoder comprising progressive reconstruction blocks capturing detailed features at various resolutions through multi-scale features obtained from various receptive fields to preserve accuracy during reconstruction. Subsequently, we propose a deep separation module that distinguishes various classes based on semantic features to use deep features to detect objects of different scales. Moreover, adjacent middle features are complemented during decoding to improve the segmentation performance. Extensive experimental results on two optical RSI datasets show that the proposed network outperforms 11 state-of-the-art methods.

Advanced Machine Learning and Deep Learning Approaches for Remote Sensing

“…However, existing remote sensing image segmentation techniques have two limitations: (1) object detection performance in various scales is poor in complex scene segmentation; (2) feature reconstruction for accurate segmentation is difficult. In order to improve this problem, the contribution by Ma et al, entitled "Deep-Separation Guided Progressive Reconstruction Network for Semantic Segmentation of Remote Sensing Images", proposed the use of a deep separation-induced progressive reconstruction network [7]. This study made two major contributions.…”

Nonuniform Correction of Ground-Based Optical Telescope Image Based on Conditional Generative Adversarial Network

“…The methods based on pre-calibration or iteration have problems such as being only suitable for a single scene or a large amount of calculation. In recent years, with the wide application of deep learning in various fields, optical problems are also more widely solved by deep learning, including optical interferometry [ 13 ], single-pixel imaging [ 14 , 15 ], wavefront sensing [ 16 , 17 , 18 ], remote sensing [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ] and Fourier ptychography [ 27 , 28 , 29 ]. Using deep learning for image enhancement in imaging systems is also more attractive [ 22 , 23 , 30 , 31 , 32 ].…”