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

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 2; p. 213
• Main Authors: Zhang, Chengming, Chen, Yan, Yang, Xiaoxia, Gao, Shuai, Li, Feng, Kong, Ailing, Zu, Dawei, Sun, Li
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2020
• Subjects: Accuracy; aerial images; Algorithms; Artificial neural networks; Automation; Cameras; Classification; Classifiers; Coders; convolutional neural network; Crops; Datasets; Feature extraction; gaofen 6; Image classification; Image processing; Image segmentation; Information processing; Land use; Methods; Model accuracy; multi-scale feature fusion; Neural networks; Object recognition; Pixels; Remote sensing; Researchers; semantic features; Semantics; Summer; tai’an; Vegetation; Wavelet transforms; Wheat; Winter; China

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...