Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data

• Published in: GIScience and remote sensing Vol. 56; no. 8; pp. 1170 - 1191
• Main Authors: Zhou, Ya'nan, Luo, Jiancheng, Feng, Li, Yang, Yingpin, Chen, Yuehong, Wu, Wei
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 17.11.2019; Taylor & Francis Group
• Subjects: agricultural land; China; crop classification; data collection; LSTM network; neural networks; precision agriculture; remote sensing; Sentienl-1 SAR; synthetic aperture radar; time series analysis; China
• ISSN: 1548-1603; 1943-7226; 1943-7226
• DOI: 10.1080/15481603.2019.1628412

Farmland parcel-based crop classification using satellite data plays an important role in precision agriculture. In this study, a deep-learning-based time-series analysis method employing optical images and synthetic aperture radar (SAR) data is presented for crop classification for cloudy and rainy regions. Central to this method is the spatial-temporal incorporation of high-resolution optical images and multi-temporal SAR data and deep-learning-based time-series analysis. First, a precise farmland parcel map is delineated from high-resolution optical images. Second, pre-processed SAR intensity images are overlaid onto the parcel map to construct time series of crop growth for each parcel. Third, a deep-learning-based (using the long short-term memory, LSTM, network) classifier is employed to learn time-series features of crops and to classify parcels to produce a final classification map. The method was applied to two datasets of high-resolution ZY-3 images and multi-temporal Sentinel-1A SAR data to classify crop types in Hunan and Guizhou of China. The classification results, with an 5.0% improvement in overall accuracy compared to those of traditional methods, illustrate the effectiveness of the proposed framework for parcel-based crop classification for southern China. A further analysis of the relationship between crop calendars and change patterns of time-series intensity indicates that the LSTM model could learn and extract useful features for time-series crop classification.