Global context information is essential for the semantic segmentation of remote sensing (RS) images. However, most existing methods rely on a convolutional neural network (CNN), which challenging to directly obtain global due locality convolution operation. Inspired by Swin transformer with powerful modeling capabilities, we propose novel framework RS images called ST-U-shaped (UNet), embeds into classical CNN-based UNet. ST-UNet constitutes dual encoder structure and CNN in parallel. First,...

Accurate and timely monitoring of biochemical biophysical traits associated with crop growth is essential for indicating status yield prediction precise field management. This study evaluated the application three combinations feature selection machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images estimating bio-parameters, including leaf area index (LAI), chlorophyll content (LCC), canopy (CCC), at key stages winter wheat. The performance Support...

Estimating anthropogenic CO2 emissions from satellite observations contributes to transparency in reporting. In this study, we proposed a method for calculating using Orbiting Carbon Observatory-2 (OCO-2) XCO2 (the column-averaged dry-air mole fraction) observations. We identified local plume enhancements on the OCO-2 track and retrieved through minimizing difference between enhancement simulated by Gaussian model based emission rates provided Open-Data Inventory Anthropogenic Dioxide...

The small satellite renaissance began in the 1980s and is changing economics of space. Technological trends have supported advancement satellites 1–500 kg range. number countries actively participating has grown substantially during past years. Satellite constellations (groups working concert) are emerging as a powerful effective application. In this paper, we focus on than can perform remote sensing or Earth observation tasks. An overview presented literature observation. aim survey...

Abstract. Soil moisture (SM) plays a critical role in the water and energy cycles of Earth system; consequently, long-term SM product with high quality is urgently needed. In this study, five products, including one microwave remote sensing – European Space Agency's Climate Change Initiative (ESA CCI) four reanalysis data sets Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Interim (ERA-Interim), National Centers Environmental Prediction (NCEP), 20th Century Project from Oceanic...

Challenges still exist in the task of object detection remote sensing images with densely distributed objects due to large variation scale and neglect relative position correlation. To address these issues, a Correlation Learning Detector based on Transformer (CLT-Det) is proposed for detecting dense images. A Attention Module (TAM) designed improve packed objects' model representation ability by learning pixel-wise attention Transformer. alleviate semantic gap caused variations scale,...

Aiming at improving the classification performance with greatly reduced annotation cost, this paper presents an active deep learning approach for minimally-supervised PolSAR image classification, which integrates and fine-tuning convolutional neural network (CNN) into a principled framework. Starting from CNN trained using very limited number of labeled pixels, we iteratively actively select most informative candidates annotation, incrementally fine-tune by incorporating newly annotated...

Monitoring the biochemical pigment contents in individual plants is crucial for assessing their health statuses and physiological states. Fast, low-cost measurements of plants’ traits have become feasible due to advances multispectral imaging sensors recent years. This study evaluated field application proximal combined with feature selection regressive analysis estimate poplar leaves. The combination 6 spectral bands 26 vegetation indices (VIs) derived from was taken as group initial...

The effectiveness of landslide disaster prevention depends largely on the quality early identification potential hazards, and how to comprehensively, deeply, accurately identify such hazards has become a major difficulty in management. Existing deep learning methods for hazard often use fixed-size window modeling ignore different sizes required by landslides scales. To address this problem, we propose an adaptive method based multisource data. Taking Yongping County, China, as study area,...

This paper aims to bring together most of the key issues involved in research novel systems telegeoprocessing. Telegeoprocessing can be defined as a new discipline based on real-time spatial databases updated regularly by means telecommunications order support problem solving and decision-making at any time place . It involves integration remote sensing, Geographic Information Systems (GIS), Global Positioning System (GPS) telecommunications. categorized WebGIS (an Internet GIS), Mobile...

Identification of potential landslide hazards is great significance for disaster prevention and control. CNN (Convolutional Neural Networks), RNN (Recurrent Networks) many other deep learning methods have been used to identify hazards. However, most samples are made with a fixed window size, which affects recognition accuracy some extent. This paper presents multi-window hidden danger identification method according the scale in experimental area. Firstly, area preliminarily screened by...

Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in processing for remote sensing applications. However, it a challenging task due to two main reasons. Firstly, the label information difficult acquire high annotation costs. Secondly, speckle effect embedded PolSAR imaging process remarkably degrades performance. To address these issues, we present contextual semantic method this paper. With newly defined channel-wise consistent feature set as...

The purpose of this study is to estimate the particulate matter (PM2.5 and PM10) in China using improved geographically temporally weighted regression (IGTWR) model Fengyun (FY-4A) aerosol optical depth (AOD) data. Based on IGTWR model, boundary layer height (BLH), relative humidity (RH), AOD, time, space, normalized difference vegetation index (NDVI) data are employed PM2.5 PM10. main processes as follows: firstly, feasibility AOD from FY-4A estimating PM10 mass concentrations were analysed...

The Advanced Geostationary Radiation Imager (AGRI) is one of the primary payloads aboard FY-4A geostationary meteorological satellite, which can provide high-frequency, wide coverage, and multiple spectral channel observations for China surrounding areas. There are currently few studies on aerosol optical depth (AOD) inversion from AGRI data. Based data, a new land AOD retrieval algorithm called band ratio library (BRL) was proposed in this study. monthly average surface reflectance...

This paper presents a novel unsupervised polarimetric synthetic aperture radar (PolSAR) image classification method, which incorporates factorization and deep convolutional networks into principled framework. To implement this idea, we design neural network (CNN) with newly defined loss function measures the probability distribution distance between initial maps CNN predictions. In proposed firstly execute to generate dictionary of meaningful atom scatters their corresponding maps, where...

In recent years, with the increasing number of Earth observation satellites and popularization application various sensors, remote sensing data have shown a rapid growth trend present typical big characteristics. The continuous enrichment has provided large information resources for science research promoted wide technology in resources, ecology, environment, energy, health, urban management, so on. However, mining from multisource heterogeneous data, which requires amount computing power,...

Near-surface NO2 (NS-NO2) is closely related to human health and the atmospheric environment. While top-down approaches have been widely applied estimate NS-NO2 using satellite-based column measurements, there still exist significant defects, resulting in a low overall fit amount of bias. This paper combines GOME-2B OMI satellite data daily with spatial resolution 0.1° × from 2014 2018 over Mainland China, machine learning method. The estimated result has four important characteristics....

Hydraulic cylinders are used as actuators of the hydraulic system cable pendulum bar in a space launch tower. Internal leakage is common failure mode that severely affects missions. Fast and accurate identification cylinder can ensure safety whole system. In this paper, wavelet analysis to extract fault features, backpropagation neural network establish classifier for intelligent faults. Experimental results show proposed method has high accuracy, providing basis realization state monitoring...

Abstract. ZiYuan-3 (ZY-3), launched in January 09, 2012, is China's first civilian high-resolution stereo mapping satellite. ZY-3 equipped with three-line scanners (nadir, backward and forward) for mapping, the resolutions of panchromatic (PAN) images are 2.1-m at nadir looking 3.6-m tilt angles ±22° forward looking, respectively. The base-height ratio 0.85-0.95. Compared from two views images, arrays can be used DSM generation taking advantage one more view than conventional photogrammetric...

Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which be used to retrieve long-term aerosol optical depth (AOD). This is great significance the study climate change. In this paper, we proposed an algorithm jointly calculate AOD land surface properties from AVHRR observations. With assumptions that doesn't vary in adjacent space earth property two days,...

Abstract The study of global climate change seeks to understand: (1) the components Earth's varying environmental system, with a particular focus on climate; (2) how these interact determine present conditions; (3) factors driving components; (4) history and projection future change; (5) knowledge about variability can be applied present-day decision-making. This paper addresses use high-performance computing high-throughput for Digital Earth (DE) platform. Two aspects (HPC)/high-throughput...

This is the preface to special issue on use of prior knowledge for quantitative remote sensing and validation results from at different spatial scales. Quantitative inverse problem retrieval geophysical biophysical parameters using remote-sensing data. usually a non-linear ill-posed problem. To overcome problems retrieval, normally used. Validation general scientific community. Frequent products necessary ensure their quality accuracy. includes articles in situ measurements field campaign,...