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Satellite image time series (SITS) is a sequence of satellite images that record a given area at several consecutive times. The aim of such sequences is to use not only spatial information but also the temporal dimension of the data, which is used for multiple real-world applications, such as classification, segmentation, anomaly detection, and prediction. Several traditional machine learning algorithms have been developed and successfully applied to time series for predictions. However, these methods have limitations in some situations, thus deep learning (DL) techniques have been introduced to achieve the best performance. Reviews of machine learning and DL methods for time series prediction problems have been conducted in previous studies. However, to the best of our knowledge, none of these surveys have addressed the specific case of works using DL techniques and satellite images as datasets for predictions. Therefore, this paper concentrates on the DL applications for SITS prediction, giving an overview of the main elements used to design and evaluate the predictive models, namely the architectures, data, optimization functions, and evaluation metrics. The reviewed DL-based models are divided into three categories, namely recurrent neural network-based models, hybrid models, and feed-forward-based models (convolutional neural networks and multi-layer perceptron). The main characteristics of satellite images and the major existing applications in the field of SITS prediction are also presented in this article. These applications include weather forecasting, precipitation nowcasting, spatio-temporal analysis, and missing data reconstruction. Finally, current limitations and proposed workable solutions related to the use of DL for SITS prediction are also highlighted. [ABSTRACT FROM AUTHOR]

The images of the Sentinel-2 constellation can help the verification process of farmers' declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study's geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows. [ABSTRACT FROM AUTHOR]

Over one hundred spatiotemporal fusion algorithms have been proposed, but convolutional neural networks trained with large amounts of data for spatiotemporal fusion have not shown significant advantages. In addition, no attention has been paid to whether fused images can be used for change detection. These two issues are addressed in this work. A new dataset consisting of nine pairs of images is designed to benchmark the accuracy of neural networks using one-pair spatiotemporal fusion with neural-network-based models. Notably, the size of each image is significantly larger compared to other datasets used to train neural networks. A comprehensive comparison of the radiometric, spectral, and structural losses is made using fourteen fusion algorithms and five datasets to illustrate the differences in the performance of spatiotemporal fusion algorithms with regard to various sensors and image sizes. A change detection experiment is conducted to test if it is feasible to detect changes in specific land covers using the fusion results. The experiment shows that convolutional neural networks can be used for one-pair spatiotemporal fusion if the sizes of individual images are adequately large. It also confirms that the spatiotemporally fused images can be used for change detection in certain scenes. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform traditional transform coding schemes in terms of rate-distortion trade-off; however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density model or a hyperprior auxiliary autoencoder to eliminate the need for prior knowledge. However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints. The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. Apart from a network dimension reduction that systematically targets each parameter of the analysis and synthesis transforms, we propose a simplified entropy model that preserves the adaptability to the input image. Indeed, a statistical analysis performed on satellite images shows that the Laplacian distribution fits most features of their representation. A complex non parametric distribution fitting or a cumbersome hyperprior auxiliary autoencoder can thus be replaced by a simple parametric estimation. The proposed complexity-reduced autoencoder outperforms the Consultative Committee for Space Data Systems standard (CCSDS 122.0-B) while maintaining a competitive performance, in terms of rate-distortion trade-off, in comparison with the state-of-the-art learned image compression schemes. [ABSTRACT FROM AUTHOR]

At present, convolutional neural networks (CNN) have been widely used in building extraction from remote sensing imagery (RSI), but there are still some bottlenecks. On the one hand, there are so many parameters in the previous network with complex structure, which will occupy lots of memories and consume much time during training process. On the other hand, low-level features extracted by shallow layers and abstract features extracted by deep layers of artificial neural network cannot be fully fused, which leads to an inaccurate building extraction from RSI. To alleviate these disadvantages, a dense residual neural network (DR-Net) was proposed in this paper. DR-Net uses a deeplabv3+Net encoder/decoder backbone, in combination with densely connected convolution neural network (DCNN) and residual network (ResNet) structure. Compared with deeplabv3+net (containing about 41 million parameters) and BRRNet (containing about 17 million parameters), DR-Net contains about 9 million parameters; So, the number of parameters reduced a lot. The experimental results for both the WHU Building Dataset and Massachusetts Building Dataset, DR-Net show better performance in building extraction than other two state-of-the-art methods. Experiments on WHU building data set showed that Intersection over Union (IoU) increased by 2.4% and F1 score increased by 1.4%; in terms of Massachusetts Building Dataset, IoU increased by 3.8% and F1 score increased by 2.9%. [ABSTRACT FROM AUTHOR]

The long-term spectral characteristics of the bare soil surface (BSS) in the BLUE, GREEN, RED, NIR, SWIR1, and SWIR2 Landsat spectral bands are poorly studied. Most often, the RED and NIR spectral bands are used to analyze the spatial heterogeneity of the soil cover; in our opinion, it is outmoded and seems unreasonable. The study of multi-temporal spectral characteristics requires the processing of big remote sensing data based on artificial intelligence in the form of convolutional neural networks. The analysis of BSS belongs to the direct methods of analysis of the soil cover. Soil degradation can be detected by ground methods (field reconnaissance surveys), modeling, or digital methods, and based on the remote sensing data (RSD) analysis. Ground methods are laborious, and modeling gives indirect results. RSD analysis can be based on the principles of calculation of vegetation indices (VIs) and on the BSS identification. The calculation of VIs also provides indirect information about the soil cover through the state of vegetation. BSS analysis is a direct method for analyzing soil cover heterogeneity. In this work, the informativeness of the long-term (37 years) average spectral characteristics of the BLUE, GREEN, RED, NIR, SWIR1 and SWIR2 bands of the Landsat 4–8 satellites for detecting areas of soil degradation with recognition of the BSS using deep machine learning methods was estimated. The objects of study are the spectral characteristics of kastanozems (dark chestnut soils) in the south of Russia in the territory of the Morozovsky district of the Rostov region. Soil degradation in this area is mainly caused by erosion. The following methods were used: retrospective monitoring of soil and land cover, deep machine learning using convolutional neural networks, and cartographic analysis. Six new maps of the average long-term spectral brightness of the BSS have been obtained. The information content of the BSS for six spectral bands has been verified on the basis of ground surveys. The informativeness was determined by the percentage of coincidences of degradation facts identified during the RSD analysis, and those determined in the field. It has been established that the spectral bands line up in the following descending order of information content: RED, NIR, GREEN, BLUE, SWIR1, SWIR2. The accuracy of degradation maps by band was determined as: RED—84.6%, NIR—82.9%, GREEN—78.0%, BLUE—78.0%, SWIR1—75.5%, SWIR2—62.2%. [ABSTRACT FROM AUTHOR]