Bridging optical and SAR satellite image time series via contrastive feature extraction for crop classification

Yuan, Yuan; Lin, Lei; Zhou, Zeng-Guang; Jiang, Hanchao; Liu, Qingshan

doi:10.1016/j.isprsjprs.2022.11.020

Cited by 27 publications

(11 citation statements)

References 40 publications

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“…The fusion of data from different sources remains a subject of study in various fields. The recent study [70] presents a new technique for crop classification using a combination of optical and SAR image time series. The method is evaluated on a large and imbalanced dataset of 18 crop types, and the results show that it outperforms state-of-the-art methods with up to 0.42% better overall accuracy and 0.53% better mIoU.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…Iou is the intersection and concurrence ratio of actual and predicted category samples, while mIou is the summed average of the intersection and concurrence ratios for each category [52]:…”

Section: Confusion Matrix Winter Wheat Non-winter Wheatmentioning

confidence: 99%

A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net

et al. 2023

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This paper focuses on the problems of omission, misclassification, and inter-adhesion due to overly dense distribution, intraclass diversity, and interclass variability when extracting winter wheat (WW) from high-resolution images. This paper proposes a deep supervised network RAunet model with multi-scale features that incorporates a dual-attention mechanism with an improved U-Net backbone network. The model mainly consists of a pyramid input layer, a modified U-Net backbone network, and a side output layer. Firstly, the pyramid input layer is used to fuse the feature information of winter wheat at different scales by constructing multiple input paths. Secondly, the Atrous Spatial Pyramid Pooling (ASPP) residual module and the Convolutional Block Attention Module (CBAM) dual-attention mechanism are added to the U-Net model to form the backbone network of the model, which enhances the feature extraction ability of the model for winter wheat information. Finally, the side output layer consists of multiple classifiers to supervise the results of different scale outputs. Using the RAunet model to extract the spatial distribution information of WW from GF-2 imagery, the experimental results showed that the mIou of the recognition results reached 92.48%, an improvement of 2.66%, 4.15%, 1.42%, 2.35%, 3.76%, and 0.47% compared to FCN, U-Net, DeepLabv3, SegNet, ResUNet, and UNet++, respectively. The superiority of the RAunet model in high-resolution images for WW extraction was verified in effectively improving the accuracy of the spatial distribution information extraction of WW.

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“…Several instances of image fusion have been considered, some works aim to directly supply classification maps from multiple satellite image and surface elevation data at each time instant [9], integrating optical and radar data for time-series crop classification [10,11], or fusing spatio-temporal optical and elevation data to obtain high-resolution land temperature maps [12].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, classification or mapping tasks based on time-series remote sensing data is receiving increasing interest in the literature [13,14,10,11]. Thus, to overcome the limitations of existing instruments, fusing images with different spectral and spatial resolutions has been extensively studied to generate images with high spatial and spectral resolutions, which are critical for accurately distinguishing different materials in a pixel [15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics

Li¹,

Duvvuri²,

Borsoi³

et al. 2023

Preprint

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Real-time satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena such as floods, earthquakes, etc. One important constraint of satellite imaging is the trade-off between spatial/spectral resolution and their revisiting time, a consequence of design and physical constraints imposed by satellite orbit among other technical limitations. In this paper, we focus on fusing multi-temporal, multi-spectral images where data acquired from different instruments with different spatial resolutions is used. We leverage the spatial relationship between images at multiple modalities to generate high-resolution image sequences at higher revisiting rates. To achieve this goal, we formulate the fusion method as a recursive state estimation problem and study its performance in filtering and smoothing contexts. Furthermore, a calibration strategy is proposed to estimate the time-varying temporal dynamics of the image sequence using only a small amount of historical image data. Differently from the training process in traditional machine learning algorithms, which usually require large datasets and computation times, the parameters of the temporal dynamical model are calibrated based on an analytical expression that uses only two of the images in the historical dataset. A distributed version of the Bayesian filtering and smoothing strategies is also proposed to reduce its computational complexity. To evaluate the proposed methodology we consider a water mapping task where real data acquired by the Landsat and MODIS instruments are fused generating high spatial-temporal resolution image estimates. Our experiments show that the proposed methodology outperforms the competing methods in both estimation accuracy and water mapping tasks.

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Bridging optical and SAR satellite image time series via contrastive feature extraction for crop classification

Cited by 27 publications

References 40 publications

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

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

A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net

Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics

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