Multi-temporal phenological indices derived from time series Sentinel-1 images to country-wide crop classification

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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Multi-Temporal Indices Derived from Time Series of Sentinel-1 Images as a Phenological Description of Plants Growing for Crop Classification

2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) ◽

10.1109/multi-temp.2019.8866905 ◽

2019 ◽

Cited By ~ 1

Author(s):

Edyta Wozniak ◽

Wlodek Kofman ◽

Sebastian Aleksandrowicz ◽

Marcin Rybicki ◽

Stanislaw Lewinski

Keyword(s):

Time Series ◽

Multi Temporal ◽

Crop Classification ◽

Temporal Indices

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Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Applied Sciences ◽

10.3390/app9040643 ◽

2019 ◽

Vol 9 (4) ◽

pp. 643 ◽

Cited By ~ 9

Author(s):

Geun-Ho Kwak ◽

No-Wook Park

Keyword(s):

Time Series ◽

Texture Features ◽

Classification Performance ◽

Support Vector ◽

Texture Information ◽

Multi Temporal ◽

Crop Classification ◽

Uav Images ◽

Uav Image ◽

The Impact

Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often used for crop classification. From a data availability viewpoint, it is not always possible to acquire time-series UAV images due to limited accessibility to the study area. Thus, it is necessary to improve classification performance for situations when a single or minimum number of UAV images are available for crop classification. In this study, we investigate the potential of gray-level co-occurrence matrix (GLCM)-based texture information for crop classification with time-series UAV images and machine learning classifiers including random forest and support vector machine. In particular, the impact of combining texture and spectral information on the classification performance is evaluated for cases that use only one UAV image or multi-temporal images as input. A case study of crop classification in Anbandegi of Korea was conducted for the above comparisons. The best classification accuracy was achieved when multi-temporal UAV images which can fully account for the growth cycles of crops were combined with GLCM-based texture features. However, the impact of the utilization of texture information was not significant. In contrast, when one August UAV image was used for crop classification, the utilization of texture information significantly affected the classification performance. Classification using texture features extracted from GLCM with larger kernel size significantly improved classification accuracy, an improvement of 7.72%p in overall accuracy for the support vector machine classifier, compared with classification based solely on spectral information. These results indicate the usefulness of texture information for classification of ultra-high-spatial-resolution UAV images, particularly when acquisition of time-series UAV images is difficult and only one UAV image is used for crop classification.

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On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

Remote Sensing ◽

10.3390/rs11070776 ◽

2019 ◽

Vol 11 (7) ◽

pp. 776 ◽

Cited By ~ 4

Author(s):

Qinghua Xie ◽

Jinfei Wang ◽

Chunhua Liao ◽

Jiali Shang ◽

Juan Lopez-Sanchez ◽

...

Keyword(s):

Time Series ◽

Random Forest ◽

Supervised Classification ◽

Alpha Angle ◽

Morphological Characteristics ◽

Random Forest Classifier ◽

Decomposition Approach ◽

Multi Temporal ◽

Crop Classification ◽

Two Parameters

In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition.

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Posterior Probability-Based Single Time-Series Model Combination Strategy for Predicting Multi-Temporal Crop Classification

2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) ◽

10.1109/ipec51340.2021.9421287 ◽

2021 ◽

Author(s):

Huiying Li ◽

Yunqiang Wu ◽

Hongshuang Lu ◽

Shengbo Chen

Keyword(s):

Time Series ◽

Posterior Probability ◽

Time Series Model ◽

Model Combination ◽

Combination Strategy ◽

Single Time ◽

Multi Temporal ◽

Crop Classification

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Detection of Pre-Failure Deformation of the 2017 Maoxian Landslide with Time-Series Insar and Multi-Temporal Optical Datasets

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323837 ◽

2020 ◽

Author(s):

Jianming Kuang ◽

Linlin Ge ◽

Alex Hay-Man Ng ◽

Zheyuan Du ◽

Qi Zhang

Keyword(s):

Time Series ◽

Multi Temporal

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Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

Remote Sensing ◽

10.3390/rs12111876 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1876 ◽

Cited By ~ 1

Author(s):

Katsuto Shimizu ◽

Tetsuji Ota ◽

Nobuya Mizoue ◽

Hideki Saito

Keyword(s):

Time Series ◽

Prediction Accuracy ◽

Time Series Data ◽

Airborne Lidar ◽

Canopy Height ◽

Forest Monitoring ◽

Series Data ◽

Landsat 8 ◽

Multi Temporal ◽

Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

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