DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal 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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Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data

GIScience & Remote Sensing ◽

10.1080/15481603.2019.1628412 ◽

2019 ◽

Vol 56 (8) ◽

pp. 1170-1191 ◽

Cited By ~ 9

Author(s):

Ya’nan Zhou ◽

Jiancheng Luo ◽

Li Feng ◽

Yingpin Yang ◽

Yuehong Chen ◽

...

Keyword(s):

High Resolution ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Optical Images ◽

Multi Temporal ◽

Long Short Term Memory ◽

Crop Classification ◽

Sar Data

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Assessment of multi-temporal RADARSAT-2 polarimetric SAR data for crop classification in an urban/rural fringe area

2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics) ◽

10.1109/argo-geoinformatics.2013.6621928 ◽

2013 ◽

Author(s):

Qin Ma ◽

Jinfei Wang ◽

Jiali Shang ◽

Peng Wang

Keyword(s):

Polarimetric Sar ◽

Multi Temporal ◽

Urban Rural ◽

Crop Classification ◽

Sar Data ◽

Fringe Area

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Using Multivariate Time Series Data via Long-Short Term Memory Network for Temperature Forecasting

Systems and Information Sciences - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-030-59194-6_4 ◽

2020 ◽

pp. 38-47

Author(s):

Jorge L. Charco ◽

Telmo Roque-Colt ◽

Kevin Egas-Arizala ◽

Charles M. Pérez-Espinoza ◽

Angélica Cruz-Chóez

Keyword(s):

Time Series ◽

Time Series Data ◽

Short Term Memory ◽

Multivariate Time Series ◽

Series Data ◽

Short Term ◽

Term Memory ◽

Temperature Forecasting ◽

Memory Network ◽

Long Short Term Memory

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Convolutional Neural Network and Long Short-Term Memory Models for Ice-Jam Prediction

10.5194/tc-2021-194 ◽

2021 ◽

Author(s):

Fatemehalsadat Madaeni ◽

Karem Chokmani ◽

Rachid Lhissou ◽

Saeid Homayuni ◽

Yves Gauthier ◽

...

Keyword(s):

Time Series ◽

Short Term Memory ◽

Weather Forecasting ◽

Multivariate Time Series ◽

Water Levels ◽

Time Series Classification ◽

Ice Jams ◽

Ice Jam ◽

Learning Techniques ◽

Long Short Term Memory

Abstract. In cold regions, ice-jam events result in severe flooding due to a rapid rise in water levels upstream of the jam. These floods threaten human safety and damage properties and infrastructures as the floods resulting from ice-jams are sudden. Hence, the ice-jam prediction tools can give an early warning to increase response time and minimize the possible corresponding damages. However, the ice-jam prediction has always been a challenging problem as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. The ice-jam prediction problem can be considered as a binary multivariate time-series classification. Deep learning techniques have been successfully applied for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied CNN, LSTM, and combined CN-LSTM networks for ice-jam prediction for all the rivers in Quebec. The results show that the CN-LSTM model yields the best results in the validation and generalization with F1 scores of 0.82 and 0.91, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of them further improves classification.

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Feature Dimension Reduction Using Stacked Sparse Auto-Encoders for Crop Classification with Multi-Temporal, Quad-Pol SAR Data

Remote Sensing ◽

10.3390/rs12020321 ◽

2020 ◽

Vol 12 (2) ◽

pp. 321

Author(s):

Jiao Guo ◽

Henghui Li ◽

Jifeng Ning ◽

Wenting Han ◽

Weitao Zhang ◽

...

Keyword(s):

Dimension Reduction ◽

Classification Accuracy ◽

Short Term Memory ◽

European Space Agency ◽

Agricultural Crop ◽

Temporal Data ◽

Multi Temporal ◽

Crop Classification ◽

Feature Dimension ◽

Feature Dimension Reduction

Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop classification accuracies since the same crop shows different external phenomena as it grows up. In practice, the utilization of multi-temporal data encounters a serious problem known as a “dimension disaster”. Aiming to solve this problem and raise the classification accuracy, this study developed a feature dimension reduction method using stacked sparse auto-encoders (S-SAEs) for crop classification. First, various incoherent scattering decomposition algorithms were employed to extract a variety of detailed and quantitative parameters from multi-temporal PolSAR data. Second, based on analyzing the configuration and main parameters for constructing an S-SAE, a three-hidden-layer S-SAE network was built to reduce the dimensionality and extract effective features to manage the “dimension disaster” caused by excessive scattering parameters, especially for multi-temporal, quad-pol SAR images. Third, a convolutional neural network (CNN) was constructed and employed to further enhance the crop classification performance. Finally, the performances of the proposed strategy were assessed with the simulated multi-temporal Sentinel-1 data for two experimental sites established by the European Space Agency (ESA). The experimental results showed that the overall accuracy with the proposed method was raised by at least 17% compared with the long short-term memory (LSTM) method in the case of a 1% training ratio. Meanwhile, for a CNN classifier, the overall accuracy was almost 4% higher than those of the principle component analysis (PCA) and locally linear embedded (LLE) methods. The comparison studies clearly demonstrated the advantage of the proposed multi-temporal crop classification methodology in terms of classification accuracy, even with small training ratios.

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In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series

Remote Sensing ◽

10.3390/rs11020118 ◽

2019 ◽

Vol 11 (2) ◽

pp. 118 ◽

Cited By ~ 16

Author(s):

Valérie Demarez ◽

Florian Helen ◽

Claire Marais-Sicre ◽

Frédéric Baup

Keyword(s):

Time Series ◽

Shuttle Radar Topography Mission ◽

Irrigation Scheduling ◽

Landsat 8 ◽

Radar Images ◽

Optical Images ◽

Multi Temporal ◽

Irrigated Crops ◽

Elevation Model ◽

The Impact

Numerous studies have reported the use of multi-spectral and multi-temporal remote sensing images to map irrigated crops. Such maps are useful for water management. The recent availability of optical and radar image time series such as the Sentinel data offers new opportunities to map land cover with high spatial and temporal resolutions. Early identification of irrigated crops is of major importance for irrigation scheduling, but the cloud coverage might significantly reduce the number of available optical images, making crop identification difficult. SAR image time series such as those provided by Sentinel-1 offer the possibility of improving early crop mapping. This paper studies the impact of the Sentinel-1 images when used jointly with optical imagery (Landsat8) and a digital elevation model of the Shuttle Radar Topography Mission (SRTM). The study site is located in a temperate zone (southwest France) with irrigated maize crops. The classifier used is the Random Forest. The combined use of the different data (radar, optical, and SRTM) improves the early classifications of the irrigated crops (k = 0.89) compared to classifications obtained using each type of data separately (k = 0.84). The use of the DEM is significant for the early stages but becomes useless once crops have reached their full development. In conclusion, compared to a “full optical” approach, the “combined” method is more robust over time as radar images permit cloudy conditions to be overcome.

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Mid-season Crop Classification Using Dual-, Compact-, and Full-polarization in Preparation for the Radarsat Constellation Mission (RCM)

Remote Sensing ◽

10.3390/rs11131582 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1582 ◽

Cited By ~ 5

Author(s):

Mahdianpari ◽

Mohammadimanesh ◽

McNairn ◽

Davidson ◽

Rezaee ◽

...

Keyword(s):

Variable Importance ◽

Sar Images ◽

Object Based ◽

Multi Temporal ◽

Importance Analysis ◽

Crop Mapping ◽

Crop Classification ◽

Sar Data ◽

Highly Correlated ◽

Marginal Improvement

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm.

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Monitoring snow and ice surfaces on King George Island, Antarctic Peninsula, with high-resolution TerraSAR-X time series

Antarctic Science ◽

10.1017/s0954102015000577 ◽

2015 ◽

Vol 28 (2) ◽

pp. 135-149 ◽

Cited By ~ 10

Author(s):

U. Falk ◽

H. Gieseke ◽

F. Kotzur ◽

M. Braun

Keyword(s):

Time Series ◽

High Resolution ◽

Snow Cover ◽

Satellite Image ◽

Polar Regions ◽

King George Island ◽

Differential Gps ◽

Snow Cover Extent ◽

Wide Range ◽

Multi Temporal

AbstractChanges of glaciers and snow cover in polar regions affect a wide range of physical and ecosystem processes on land and in the adjacent marine environment. In this study, we investigated the potential of 11-day repeat high-resolution satellite image time series from the TerraSAR-X mission to derive glaciological and hydrological parameters on King George Island, Antarctica, between 25 October 2010 and 19 April 2011. The spatial pattern and temporal evolution of snow cover extent on ice-free areas can be monitored using multi-temporal coherence images. Synthetic aperture radar (SAR) coherence is used to map glacier extent of land-terminating glaciers with an average accuracy of 25 m. Multi-temporal SAR colour composites identify the position of the late summer snow line at ~220 m a.s.l. Glacier surface velocities are obtained from intensity feature-tracking. Surface velocities near the calving front of Fourcade Glacier were up to 1.8±0.01 m d-1. Using an intercept theorem based on fundamental geometric principles together with differential GPS field measurements, the ice discharge of Fourcade Glacier was estimated at 20 700±5500 m3 d-1 (corresponding to ~19±5 kt d-1). The rapidly changing surface conditions on King George Island and the lack of high-resolution digital elevation models for the region remain restrictions for the applicability of SAR data and the precision of derived products. Supplemental data are available at http://dx.doi.org/10.1594/PANGAEA.853954.

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