Paper Info
Title
Classification Of Very-High-Spatial-Resolution Aerial Images Based On Multiscale Features With Limited Semantic Information
Abstract
Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).
Year
DOI
Venue
2021
10.3390/rs13030364
REMOTE SENSING
Keywords
DocType
Volume
deep learning, aerial imagery, convolutional neural network, object-based classification
Journal
13
Issue
Citations 
PageRank 
3
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Han Gao100.68
Jinhui Guo200.34
Peng Guo301.35
Xiuwan Chen43318.04