Recently, convolutional neural networks (CNNs) have been widely applied to hyperspectral image (HSI) classification due to their detailed representation of features. Nevertheless, the current CNN-based HSI classification methods mainly follow a patch-based learning framework. These methods are nonglobal learning methods, which not only limit the use of global information but also require a high computational cost. In this letter, an image-based global learning framework is introduced to HSI classification. Based on this framework, we propose a dual-channel convolutional network (DCCN) for HSI classification to maximize the exploitation of the global and multiscale information of HSI. The experimental results conducted on two real hyperspectral datasets indicate that our method is superior to other related methods in terms of both efficiency and accuracy for HSI classification.

Dual-Channel Convolution Network With Image-Based Global Learning Framework for Hyperspectral Image Classification

Accurate water extraction and quantitative estimation of water quality are two key and challenging issues for remote sensing of water environment. Recent advances in remote sensing big data, cloud computing, and machine learning have promoted these two fields into a new era. This study reviews the operating framework and methods of remote sensing big data for water environment monitoring, with emphasis on water extraction and quantitative estimation of water quality. The following aspects were investigated in this study: (a) image data source and model evaluation metrics; (b) state-of-the-art methods for water extraction, including threshold-based methods, water indices, and machine learning-based methods; (c) state-of-the-art models for quantitative estimation of water quality, including empirical models, semi-empirical/semi-analytical models, and machine learning-based models; (d) some shortcomings and three challenges of current remote sensing big data for water environment monitoring, namely the new data gap caused by massive heterogeneous data, inefficient water environment monitoring due to “low spatiotemporal resolution,” and low accuracy of water quality estimation models resulting from complex water composition and insufficient atmospheric correction methods for water bodies; and (e) five recommendations to solve these challenges, namely, using cloud computing and emerging sensors/platforms to monitor water changes in intensive time series, establishing models based on ensemble machine learning algorithms, exploring quantitative estimation models of water quality that couple physics and causality, identifying the missing elements in water environment assessments, and developing new governance models to meet the widespread applications of remote sensing of water environment. This review can help provide a potential roadmap and information support for researchers, practitioners, and management departments in the theoretical exploration and innovative application of remote sensing big data for water environment monitoring. 

Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects

Accurate water extraction and quantitative estimation of water quality are two key and challenging issues for remote sensing of water environment. Recent advances in remote sensing big data, cloud computing, and machine learning have promoted these two fields into a new era. This study reviews the operating framework and methods of remote sensing big data for water environment monitoring, with emphasis on water extraction and quantitative estimation of water quality. The following aspects were investigated in this study: (a) image data source and model evaluation metrics; (b) state‐of‐the‐art methods for water extraction, including threshold‐based methods, water indices, and machine learning‐based methods; (c) state‐of‐the‐art models for quantitative estimation of water quality, including empirical models, semi‐empirical/semi‐analytical models, and machine learning‐based models; (d) some shortcomings and three challenges of current remote sensing big data for water environment monitoring, namely the new data gap caused by massive heterogeneous data, inefficient water environment monitoring due to “low spatiotemporal resolution,” and low accuracy of water quality estimation models resulting from complex water composition and insufficient atmospheric correction methods for water bodies; and (e) five recommendations to solve these challenges, namely, using cloud computing and emerging sensors/platforms to monitor water changes in intensive time series, establishing models based on ensemble machine learning algorithms, exploring quantitative estimation models of water quality that couple physics and causality, identifying the missing elements in water environment assessments, and developing new governance models to meet the widespread applications of remote sensing of water environment. This review can help provide a potential roadmap and information support for researchers, practitioners, and management departments in the theoretical exploration and innovative application of remote sensing big data for water environment monitoring.

The highly correlated spectral features and the limited training samples pose challenges in hyperspectral image classification. In this article, to tackle the issues of end-to-end feature learning and transfer learning with limited labeled samples, we propose a unified multiscale learning (UML) framework, which is based on a fully convolutional network. A multiscale spatial-channel attention mechanism and a multiscale shuffle block are proposed in the UML framework to improve the problem of land-cover map distortion. The contextual information and the spectral feature are enhanced before the last classification layer based on three strategies in this work: 1) the channel shuffle operation, which was employed to learn the more effective spectral characteristics by disordering the channels of the feature map; 2) multiscale block, which considered the contextual information in multiple ranges; and 3) spatiospectral attention, which enhanced the expression of the important characteristic among all pixels. Three hyperspectral datasets, including two airborne hyperspectral images and one spaceborne hyperspectral image, were used to demonstrate the performance of the UML framework in both classification and transfer learning. The experimental results confirmed that the proposed method outperforms most of the state-of-the-art hyperspectral image classification methods. The source code is released at https://github.com/Hyper-NN/UML.

A Unified Multiscale Learning Framework for Hyperspectral Image Classification

The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.

Synergetic Classification of Coastal Wetlands over the Yellow River Delta with GF-3 Full-Polarization SAR and Zhuhai-1 OHS Hyperspectral Remote Sensing

The visible-shortwave infrared Advanced Hyperspectral Imager (AHSI) is a payload onboard the Gaofen-5 satellite, which is China’s first hyperspectral satellite and is part of the Chinese High-Resolution Earth Observation System. As a supplement to the onboard radiometric calibration of the AHSI instrument, vicarious calibration is also required, which is independent of the instrument-based calibration. In this article, a reflectance-based vicarious calibration approach is presented, which takes surface reflectance data, aerosol data, and atmospheric water vapor data into account. The Dunhuang test site, which is one of the China Radiometric Calibration Sites (CRCS) for the vicarious calibration of spaceborne sensors, possesses stable, uniform, and measurable surface objects, so it was chosen as the radiation source to replace the laboratory and onboard calibrators. A Spectra Vista Corporation (SVC) spectral radiometer and a CE318 sun photometer were utilized for the measurement of the surface reflectance and the condition of the aerosol, respectively. The radiance at the entrance pupil at the top of atmosphere was then obtained through the MODerate resolution atmospheric TRANsmission (MODTRAN) atmospheric transmission model. The surface reflectance was obtained using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric model for validation. The results show that, with regard to the calibration coefficients, the calibrated AHSI instrument presents a stable radiometric performance among different land-cover types. The ratios on all the bands are between 0.8 and 1.2 and are consistent with the reflectance data from the Dunhuang test site. The ${R} ^{{2}}$ values are all greater than 0.95 and the spectral angle is all less than 2°. The standard deviations of the ratios are less than 3% for each chosen band, which proves that the calibrated data have a high consistency with the in situ measurements. When compared with Landsat 8 and Sentinel-2, the mean errors of the surface reflectance are all under 0.06, which further demonstrates that the calibrated reflectance has a high accuracy.

Juan Yuan

Papers

Vicarious Calibration for the AHSI Instrument of Gaofen-5 With Reference to the CRCS Dunhuang Test Site