An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

Li, Jingtao; Shen, You-Gen; Yang, Chao

doi:10.3390/rs13010065

Cited by 33 publications

(11 citation statements)

References 34 publications

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“…The proposed strategy of using the prior information derived from trend and seasonality to refine the transform coefficients accurately preserves the cop-specific features [54], [59]. Unlike the existing approaches that require a lot of actual training samples, the proposed approaches give good results even when actual training samples are pretty scarce [9], [65]. The generalization capability of the proposed approach can be attributed to the non-DL-based strategy of using entropy and transforms to model the characteristic features.…”

Section: B Improvement In Classification Resultsmentioning

confidence: 99%

Augmentation of Vegetation Index Curves Considering the Crop-Specific Phenological Characteristics

Arun

Karnieli

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The state-of-the-art crop phenological classifiers use vegetation index (VI) time-series data and deep learning (DL) techniques. However, the scarcity of training samples limits the performance of these approaches. Unlike the conventional augmentation techniques, the data augmentation of VI curves should preserve the crop-specific phenological events. The DLbased augmentation approaches do not give good results when the training samples are limited. Also, the conventional approaches such as translation, rotation, scaling, and wrapping do not preserve the characteristic features of the index curves, thereby making them inappropriate for the VI-curve-based augmentations. This study proposes a non-DL-based data augmentation strategy that requires only a minimal number of actual training samples. In the proposed approach, the periodic phenological events and the underlying trend for each crop class are modeled to improve the augmentation. The trends of different crop classes are estimated by jointly maximizing the autocorrelation and variance, while the optimal subsequences are generalized as the phenological events. The proposed augmentation strategy of using Maximal Overlap Discrete Wavelet Transform (MODWT) for obtaining the surrogates that retain the crop-specific features and periodicities significantly improves the results. It may be noted that the proposed approach does not alter the wavelet coefficients that are characteristics of a given crop class. The experiments using time series VI data, covering 90 fields of wheat and 60 fields of barley, confirm better accuracy of the proposed augmentation approaches as compared to the prominent approaches.

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Section: B Improvement In Classification Resultsmentioning

confidence: 99%

Augmentation of Vegetation Index Curves Considering the Crop-Specific Phenological Characteristics

Arun

Karnieli

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In addition, the authors utilized self-organizing Kohonen maps (SOMs) to reconstruct missing data due to cloudy holes. [25] 2020 LSTM, MLP, U-net Sentinel-1, Sentinel-2 ( 14) [32] 2021 ANN Sentinel-2 (4) [33] 2021 PSE + LTAE Sentinel-2 (20) [34] 2021 Bi-LSTM, LSTM Sentinel-2 ( 16) [35] 2021 CNN Sentinel-2 (11) [36] 2021 CNN-CRF, CNN Sentinel-1 (9) [37] 2021 MSFCN, CNN, Sentinel-1 ( 14) [38] 2021 LSTM, CNN, GAN Landsat-8 (3) [39] 2021 CNN AgriSAR (6) [40] 2022 CNN Sentinel2-Agri ( 20)…”

Section: Crop Classification Using Satellite Datamentioning

confidence: 99%

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

et al. 2023

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In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

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“…In computer vision, images with medium and high accuracy have become the data types commonly used by researchers, especially in species identification and classification in agriculture, such as agricultural vegetation classification, land use classification, crop classification, tree species identification, etc., ( Roslim et al, 2021 ; Chen Z. et al, 2021 ; Li et al, 2021 ; Yan et al, 2021 ). To solve the common problems of zoom sensing image segmentation algorithms, such as poor robustness, easy loss of edge information and narrow scope of application, the core task of zoom sensing image target detection is to judge whether there is a target in zoom sensing images and to detect, segment, extract and classify it.…”

Section: Related Workmentioning

confidence: 99%

Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm

Shen

Shi

Ren

et al. 2022

Front. Bioeng. Biotechnol.

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With the development of bionic computer vision for images processing, researchers have easily obtained high-resolution zoom sensing images. The development of drones equipped with high-definition cameras has greatly increased the sample size and image segmentation and target detection are important links during the process of image information. As biomimetic remote sensing images are usually prone to blur distortion and distortion in the imaging, transmission and processing stages, this paper improves the vertical grid number of the YOLO algorithm. Firstly, the light and shade of a high-resolution zoom sensing image were abstracted, and the grey-level cooccurrence matrix extracted feature parameters to quantitatively describe the texture characteristics of the zoom sensing image. The Simple Linear Iterative Clustering (SLIC) superpixel segmentation method was used to achieve the segmentation of light/dark scenes, and the saliency area was obtained. Secondly, a high-resolution zoom sensing image model for segmenting light and dark scenes was established to made the dataset meet the recognition standard. Due to the refraction of the light passing through the lens and other factors, the difference of the contour boundary light and dark value between the target pixel and the background pixel would make it difficult to detect the target, and the pixels of the main part of the separated image would be sharper for edge detection. Thirdly, a YOLO algorithm with an improved vertical grid number was proposed to detect the target in real time on the processed superpixel image array. The adjusted aspect ratio of the target in the remote sensing image modified the number of vertical grids in the YOLO network structure by using 20 convolutional layers and five maximum aggregation layers, which was more accurately adapted to “short and coarse” of the identified object in the information density. Finally, through comparison with the improved algorithm and other mainstream algorithms in different environments, the test results on the aid dataset showed that in the target detection of high spatial resolution zoom sensing images, the algorithm in this paper showed higher accuracy than the YOLO algorithm and had real-time performance and detection accuracy.

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An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

Cited by 33 publications

References 34 publications

Augmentation of Vegetation Index Curves Considering the Crop-Specific Phenological Characteristics

Augmentation of Vegetation Index Curves Considering the Crop-Specific Phenological Characteristics

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm

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