Navigating Newcastle’s Forex And Mining Scene: Tips For Success – Remote Sensing Monitoring of Post-Fire Vegetation Dynamics in the Great Xinggan Mountain Range Based on Long-Term Time Series Data: Analysis of the Effects of Six Topographic and Climate Factors.
A low-level road extraction method using SDG-DenseNet based on fusion of optical and SAR images at high resolution.
Navigating Newcastle’s Forex And Mining Scene: Tips For Success
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Xiao Xiao Xiao Xiao Scilit Preprints.org Google Scholar, Changjiang Li Changjian Li Scilit Preprints.org Google Scholar and Yinji Lei Yinji Lei Scilit Preprints.org Google Scholar *
Received: 24 May 2022 / Revised: 13 June 2022 / Accepted: 15 June 2022 / Published: 21 June 2022
Despite the increasing number of spaceborne synthetic aperture radar (SAR) images and optical images, only a few annotations can be used for scene classification tasks based on convolutional neural networks (CNN). For this situation, self-supervised methods can improve scene classification accuracy through learning recommendations from unknown data. However, self-supervised scene classification algorithms are difficult to implement on satellites due to the high computational cost. To solve this problem, we propose a simple, yet efficient, self-supervised (Lite-SRL) algorithm for the task of scene classification. First, we design a lightweight contrast learning framework for Lite-SRL, use a stochastic enhancement strategy to extract extended scenes from unknown space marine images, and Lite-SRL leverages the similarity of extended scenes to learn value propositions. Then, we adopt a stop-gradient operation to make the Lite-SRL training process not rely on large queues or negative samples, which can reduce the computational consumption. In addition, to deploy Lite-SRL on low-power onboard computing platforms, we provide a distributed hybrid parallelism (DHP) framework and a compute workload balancing (CWB) module for Lite-SRL. Experiments on representative datasets OpenSARUrban, WHU-SAR6, NWPU-Resisc45, and AID show that Lite-SRL can improve scene classification accuracy under limited interpretation data, and it can be generalized to both SAR and optical images. At the same time, compared with six state-of-the-art self-control algorithms, Lite-SRL has clear advantages in overall accuracy, number of parameters, memory consumption, and training delay. Finally, to evaluate the on-board operational capability of the proposed work, we transfer Lite-SRL to a low-power computing platform NVIDIA Jetson TX2.
The task of remote sensing scene classification (RSSC) aims to classify scene areas into different semantic categories [1, 2, 3, 4, 5], which plays an important role in various Earth observation applications, such as land resource exploration, forest inventory, and urban-district monitoring. [6, 7, 8]. In recent years, Landsat, Sentinel and other missions have augmented spaceborne marine imagery for the task of scene classification, including synthetic aperture radar (SAR) images and optical images. With the available data, scene classification methods based on convolutional neural networks have undergone rapid development [7, 9].
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However, the amount of annotated scenes for supervised CNN training remains limited. Taking SAR data as an example, SAR images are affected by color noise due to the imaging mechanism, resulting in poor image quality [ 10 , 11 ]. In addition, the random variation of pixels makes it difficult to distinguish scene categories [12]. Therefore, interpretation of SAR images requires experienced experts and is a time-consuming task [13]. There is the same problem of high annotation costs for optical images. This results in the total number of images in RSSC datasets, such as OpenSARUrban [14], WHU-SAR6 [11], NWPU-Resisc45 [3], and AID [15], being much smaller compared to natural image databases such as ImageNet [16]. to be; The number of specific images for each dataset is shown in Figure A1. With limited annotated samples, CNN overestimates after training, leading to poor generalization performance in the RSSC task. Therefore, it is interesting to explore ways to reduce the reliance of the RSSC task on descriptive information.
Recently, self-storage (SSL) has emerged as an interesting candidate to solve the known scarcity problem [18]. SSL methods can learn value propositions from unlabeled images by solving reference tasks [19]; A self-supervised network can be used as a pre-trained model to ensure high accuracy with fewer training samples [20]. To this end, a number of RSSC studies have focused on SSL. In practice, remote sensing images (RSI) are significantly different from natural images at the acquisition and transmission stages—RSIs suffer from noise effects and high transmission costs [21]. Self-control in satellites can solve these problems; however, existing SSL algorithms are difficult and computationally intensive to deploy on satellites. A method based on self-supervised instance discrimination [22] was first applied to the RSSC task; later, the SSL algorithm presented with contrastive multimedia coding showed good performance in RSSC tasks. These methods rely on a large batch of negative samples, and the training process must maintain large queues, which can consume a lot of computing resources. Other self-monitoring methods use images with the same geographic coordinates from different time periods and incorporate loss functions based on geographic coordinates with complex feature extraction modules, which also consume a lot of resources during training. Therefore, it is important to minimize the use of computing during self-monitoring.
As mentioned above, we are trying to install a self-driving algorithm on satellites. A light network is required, and practical training on board can also help. Since it is not possible to carry high-powered GPUs on satellites, the current trend is to use external devices, namely NVIDIA Jetson TX2 [24] onboard computing devices [25]. The latest radiation is characterized by on-board computing modules, the S-A1760 Venus [26], which uses TX2 within the product to help the spacecraft achieve high-performance AI computing. Therefore, we also use TX2 as a deployment platform. Under resource-constrained scenarios (limited memory, i.e. 8 GB memory size, limited computing resources, i.e. 59.7 GB/S bandwidth), distributed strategies are commonly used to train the network; Thus, a flexible distributed learning platform is needed for onboarding training. However, the approaches adopted in deep learning, such as PyTorch [27], TensorFlow [28], and Caffe [29], remain primitive for distributed training. Dedicated distributed learning frameworks such as Mesh-TensorFlow [30] and Nemesyst [31] are also not available for on-board scenarios because they do not consider the case of limited on-board computing resources.
Based on the above review, we need a self-control algorithm that simultaneously satisfies (i) guaranteed accuracy and low computational consumption. and (ii) an effective distributed strategy for self-control on board. To address these issues, we propose a lightweight onboard self-supervised representation learning (Lite-SRL) algorithm for the RSSC task. Lite-SRL uses a contrastive learning structure that includes lightweight modules to maximize the similarity of extended RSIs to extract distinct features from unknown images. The enhancement strategy used to obtain contrast images is slightly different between SAR and optical imaging. Meanwhile, inspired by BYOL [32] and SimSiam [33] self-checking algorithm, we use a stop-gradient operation, which does not rely on large batch size, queues or negative sample pairs, which reduces the computational workload. with guaranteed accuracy. In addition, the structure of Lite-SRL is suitable for distributed training for deployment. Experiments on scene classification datasets such as OpenSARUrban, WHU-SAR6, NWPU-Resisc45, and AID dataset show that Lite-SRL can improve scene classification accuracy with limited descriptive data; it also shows that Lite-SRL generalizes to both SAR and optical images in the RSSC task. At the same time, experiments with six state-of-the-art self-control algorithms show that Lite-SRL has clear advantages in terms of overall accuracy, number of parameters, memory usage, and training latency.
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To implement the Lite-SRL algorithm on the low-power computing platform Jetson TX2, we present a distributed hybrid parallelism (DHP) training framework with a generic training computation workload balancing (CWB) module. Since one TX2 node cannot complete the entire network training, CWB divides the network according to the principle of workload balancing (see Algorithm for details) and assigns each part to DHP to implement distributed hybrid parallelism training. The integration of CWB and DHP enables the training of neural networks under on-board resources.
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