Paper Info
Title
DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data.
Abstract
Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification.
Year
DOI
Venue
2019
10.3390/rs11131619
REMOTE SENSING
Keywords
Field
DocType
Sentienl-1 SAR,deep learning,spatial texture feature,time-series analysis,crop classification
Computer vision,Remote sensing,Artificial intelligence,Geology
Journal
Volume
Issue
Citations 
11
13
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yanan Zhou100.34
Jian-Cheng Luo29920.75
Li Feng300.68
Xiaocheng Zhou400.34