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Open AccessArticle

Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion

by Chengming Zhang 1,2,3,*,†, Yan Chen 1,3, Xiaoxia Yang 1,†, Shuai Gao 4, Feng Li 5, Ailing Kong 1, Dawei Zu 1 and Li Sun 1
1
College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, China
2
Key Open Laboratory of Arid Climate Change and Disaster Reduction of CMA, 2070 Donggangdong Road, Lanzhou 730020, China
3
Shandong Technology and Engineering Center for Digital Agriculture, 61 Daizong Road, Taian 271000, China
4
Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, 9 Dengzhuangnan Road, Beijing 100094, China
5
Shandong Provincal Climate Center, No. 12 Wuying Mountain Road, Jinan 250001, China
*
Author to whom correspondence should be addressed.
These authors are co-first authors as they contributed equally to this work.
Remote Sens. 2020, 12(2), 213; https://doi.org/10.3390/rs12020213
Received: 25 November 2019 / Revised: 17 December 2019 / Accepted: 6 January 2020 / Published: 8 January 2020
When extracting land-use information from remote sensing imagery using image segmentation, obtaining fine edges for extracted objects is a key problem that is yet to be solved. In this study, we developed a new weight feature value convolutional neural network (WFCNN) to perform fine remote sensing image segmentation and extract improved land-use information from remote sensing imagery. The WFCNN includes one encoder and one classifier. The encoder obtains a set of spectral features and five levels of semantic features. It uses the linear fusion method to hierarchically fuse the semantic features, employs an adjustment layer to optimize every level of fused features to ensure the stability of the pixel features, and combines the fused semantic and spectral features to form a feature graph. The classifier then uses a Softmax model to perform pixel-by-pixel classification. The WFCNN was trained using a stochastic gradient descent algorithm; the former and two variants were subject to experimental testing based on Gaofen 6 images and aerial images that compared them with the commonly used SegNet, U-NET, and RefineNet models. The accuracy, precision, recall, and F1-Score of the WFCNN were higher than those of the other models, indicating certain advantages in pixel-by-pixel segmentation. The results clearly show that the WFCNN can improve the accuracy and automation level of large-scale land-use mapping and the extraction of other information using remote sensing imagery. View Full-Text
Keywords: convolutional neural network; image segmentation; multi-scale feature fusion; semantic features; Gaofen 6; aerial images; land-use; Tai’an convolutional neural network; image segmentation; multi-scale feature fusion; semantic features; Gaofen 6; aerial images; land-use; Tai’an
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MDPI and ACS Style

Zhang, C.; Chen, Y.; Yang, X.; Gao, S.; Li, F.; Kong, A.; Zu, D.; Sun, L. Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion. Remote Sens. 2020, 12, 213.

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