Convolutional neural networks for on-board cloud screening

AcloudscreeningunitonasatelliteplatformforEarthobservationcanplayanimportant role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art

Image super-resolution with dense-sampling residual channel-spatial attention networks for multi-temporal remote sensing image classification

Image super-resolution (SR) techniques can benefit a wide range of applications in the remote sensing (RS) community, including image classification. This issue is particularly relevant for image classification on time series data, considering RS datasets that feature long temporal coverage generally have a limited spatial resolution. Recent advances in deep learning brought new opportunities for enhancing the spatial resolution of historic RS data. Numerous convolutional neural network (CNN)-based methods showed superior performance in terms of developing efficient end-to-end SR models for natural images. However, such models were rarely exploited for promoting image classification based on multispectral RS data. This paper proposes a novel CNNbased framework to enhance the spatial resolution of time series multispectral RS images. Thereby, the proposed SR model employs Residual Channel Attention Networks (RCAN) as a backbone structure, whereas based on this structure the proposed models uniquely integrate tailored channel-spatial attention and dense-sampling mechanisms for performance improvement. Subsequently, state-of-the-art CNN-based classifiers are incorporated to produce classification maps based on the enhanced time series data. The experiments proved that the proposed SR model can enable unambiguously better performance compared to RCAN and other (deep learning-based) SR techniques, especially in a domain adaptation context, i.e., leveraging Sentinel-2 images for generating SR Landsat images. Furthermore, the experimental results confirmed that the enhanced multi-temporal RS images can bring substantial improvement on fine-grained multi-temporal land use classification

A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure