A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets

An early crop classification method is functionally required in a near-real-time crop-yield prediction system, especially for upland crops. This study proposes methods to estimate the mixed-pixel ratio of corn, soybean, and other classes within a low-resolution MODIS pixel by coupling MODIS-derived crop phenology information and the past Cropland Data Layer in a random-forest regression algorithm. Verification of the classification accuracy was conducted for the Midwestern United States. The following conclusions are drawn: The use of the random-forest algorithm is effective in estimating the mixed-pixel ratio, which leads to stable classification accuracy; the fusion of historical data and MODIS-derived crop phenology information provides much better crop classification accuracy than when these are used individually; and the input of a longer MODIS data period can improve classification accuracy, especially after day of year 279, because of improved estimation accuracy for the soybean emergence date.

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Crop Classification from Sentinel-2 Time Series with Temporal Convolutional Neural Networks

10.1109/igarss47720.2021.9554358 ◽

2021 ◽

Author(s):

Sara Perez-Carabaza ◽

Vasileios Syrris ◽

Pieter Kempeneers ◽

Pierre Soille

Keyword(s):

Neural Networks ◽

Time Series ◽

Convolutional Neural Networks ◽

Crop Classification ◽

Sentinel 2

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DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data

Remote Sensing ◽

10.3390/rs11131619 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1619 ◽

Cited By ~ 3

Author(s):

Zhou Ya’nan ◽

Luo Jiancheng ◽

Feng Li ◽

Zhou Xiaocheng

Keyword(s):

Time Series ◽

High Resolution ◽

Short Term Memory ◽

Multivariate Time Series ◽

Hunan Province ◽

Spatial Features ◽

Optical Images ◽

Multi Temporal ◽

Crop Classification ◽

Sar Data

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.

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Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images

Remote Sensing ◽

10.3390/rs12223733 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3733

Author(s):

Wei Liu ◽

Jian Wang ◽

Jiancheng Luo ◽

Zhifeng Wu ◽

Jingdong Chen ◽

...

Keyword(s):

Time Series ◽

Time Profile ◽

Classification Method ◽

Guizhou Province ◽

Optical Data ◽

Time Series Classification ◽

Mountainous Areas ◽

Mountain Areas ◽

Actual Distribution ◽

Sar Data

Accurate, timely, and reliable farmland mapping is a prerequisite for agricultural management and environmental assessment in mountainous areas. However, in these areas, high spatial heterogeneity and diversified planting structures together generate various small farmland parcels with irregular shapes that are difficult to accurately delineate. In addition, the absence of optical data caused by the cloudy and rainy climate impedes the use of time-series optical data to distinguish farmland from other land use types. Automatic delineation of farmland parcels in mountain areas is still a very difficult task. This paper proposes an innovative precise farmland parcel extraction approach supported by very high resolution(VHR) optical image and time series synthetic aperture radar(SAR) data. Firstly, Google satellite imagery with a spatial resolution of 0.55 m was used for delineating the boundaries of ground parcel objects in mountainous areas by a hierarchical extraction scheme. This scheme divides farmland into four types based on the morphological features presented in optical imagery, and designs different extraction models to produce each farmland type, respectively. The potential farmland parcel distribution map is then obtained by the layered recombination of these four farmland types. Subsequently, the time profile of each parcel in this map was constructed by five radar variables from the Sentinel-1A dataset, and the time-series classification method was used to distinguish farmland parcels from other types. An experiment was carried out in the north of Guiyang City, Guizhou Province, Southwest China. The result shows that, the producer’s accuracy of farmland parcels obtained by the hierarchical scheme is increased by 7.39% to 96.38% compared with that without this scheme, and the time-series classification method produces an accuracy of 80.83% to further obtain the final overall accuracy of 96.05% for the farmland parcel maps, showing a good performance. In addition, through visual inspection, this method has a better suppression effect on background noise in mountainous areas, and the extracted farmland parcels are closer to the actual distribution of the ground farmland.

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