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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 8756

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Special Issue Editors

Dr. Ping Tang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: satellite image processing; Chinese satellites ARD

Dr. Lian-Zhi Huo

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing; machine learning; land cover mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Hankui Zhang

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Guest Editor

Geospatial Sciences Center of Excellence (GSCE), 1021 Medary Ave, Wecota Hall 115, Box 506B, Brookings, SD 57007, USA
Interests: remote sensing; machine vision; GIS development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since 2019, China has adopted an open data policy for Gaofen (GF) satellites, which grants the global community free and open data access. As a first step, global data with 16-meter resolution from GF 1 and GF 6 satellites have been made publicly accessible. The released data are in the Level 1 processing stage, which means that end users will inevitably face different preprocessing steps for these data before they are able to explore domain application tasks. To improve the usability of the shared data for end users, Gaofen 16m Analysis Ready Data (ARD) is seriously needed in order to introduce a series of standard preprocessing procedures. In this Special Issue, we invite submissions related to Gaofen 16m ARD techniques, including, but not limited to:

Geometric correction of Gaofen 16m data;
Atmospheric correction of Gaofen 16m data;
Cloud/Shadow/Water masking of Gaofen 16m data;
Quality labeling of Gaofen 16m data;
Combined use of Gaofen 16m ARD and other similar sensors (e.g., Landsat, Sentinel-2) and fusion approaches;
Suitability of Gaofen 16m ARD for LCLU classification, change detection, and time series analysis.

Dr. Ping Tang
Dr. Lian-Zhi Huo
Dr. Hankui Zhang
Guest Editors

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Keywords

Gaofen 16m data
geometric correction
radiometric correction
atmospheric correction
quality mask

Published Papers (5 papers)

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Research

24 pages, 20309 KiB

Open AccessFeature PaperArticle

Does the Rational Function Model’s Accuracy for GF1 and GF6 WFV Images Satisfy Practical Requirements?

by Xiaojun Shan and Jingyi Zhang

Remote Sens. 2023, 15(11), 2820; https://doi.org/10.3390/rs15112820 - 29 May 2023

Viewed by 1209

Abstract

The Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites have acquired many GF-1 and GF-6 wide-field-view (WFV) images. These images have been made available for free use globally. The GF-1 WFV (GF-1) and GF-6 WFV (GF-6) images have rational polynomial coefficients (RPCs). In practical applications, RPC corrections of GF-1 and GF-6 images need to be completed using the rational function model (RFM). However, can the accuracy of the rational function model satisfy practical application requirements? To address this issue, a geometric accuracy method was proposed in this paper to evaluate the accuracy of the RFM of GF-1 and GF-6 images. First, RPC corrections were completed using the RFM and refined RFM, respectively. The RFM was constructed using the RPCs and Shuttle Radar Topography Mission (SRTM) 90 m DEM. The RFM was refined via affine transformation based on control points (CPs), which resulted in a refined RFM. Then, an automatic matching method was proposed to complete the automatic matching of GF-1/GF-6 images and reference images, which enabled us to obtain many uniformly distributed CPs. Finally, these CPs were used to evaluate the geometric accuracy of the RFM and refined RFM. The 14th-layer Google images of the corresponding area were used as reference images. In the experiments, the advantages and disadvantages of BRIEF, SIFT, and the proposed method were first compared. Then, the values of the root mean square error (RSME) of 10,561 Chinese, French, and Brazilian GF-1 and GF-6 images were calculated and statistically analyzed, and the local geometric distortions of the GF-1 and GF-6 images were evaluated; these were used to evaluate the accuracy of the RFM. Last, the accuracy of the refined RFM was evaluated using the eight GF-1 and GF-6 images. The experimental results indicate that the accuracy of the RFM for most GF-1 and GF-6 images cannot meet the actual use requirement of being better than 1.0 pixel, the accuracy of the refined RFM for GF-1 images cannot meet practical requirement of being better than 1.0 pixel, and the accuracy of the refined RFM for most GF-6 images meets the practical requirement of being better than 1.0 pixel. However, the RMSE values that meet the requirements are between 0.9 and 1.0, and the geometric accuracy can be further improved. Full article

(This article belongs to the Special Issue Gaofen 16m Analysis Ready Data)

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22 pages, 7964 KiB

Open AccessArticle

Bandpass Alignment from Sentinel-2 to Gaofen-1 ARD Products with UNet-Induced Tile-Adaptive Lookup Tables

by Zhi-Qiang Liu, Zhao Wang, Zhitao Zhao, Lianzhi Huo, Ping Tang and Zheng Zhang

Remote Sens. 2023, 15(10), 2563; https://doi.org/10.3390/rs15102563 - 14 May 2023

Cited by 2 | Viewed by 1304

Abstract

The successful launching of more satellites in recent years has made data fusion an important and promising task because it can significantly increase the temporal frequency of the resulting time series data. To improve the usability of Gaofen-1 analysis ready data (GF1-ARD), Sentinel-2 (S2) is selected to enhance the temporal resolution of GF1-ARD due to their similar characteristics and short revisit period. Before constructing a denser time series from different platforms, bandpass alignment is required. Most researchers implement bandpass alignment using the linear model. However, the transformed bands of S2 by the linear model cannot match GF1-ARD well due to the limited globally shared parameters. In contrast, local-derived lookup tables (LUTs) can better address this problem. Inspired by the powerful capability of deep learning, we develop a model based on the U-shaped network (UNet) to learn tile-adaptive LUTs. Specifically, the LUTs are adaptively learned from the histogram of the S2 tile. Given that the bandpass alignment can be viewed as a histogram matching process, the expected LUTs are believed to be highly correlated with the input histogram. In addition, a simple convolutional module is further introduced to address the pixel-level misregistration. We have created a large-scale dataset and conducted extensive experiments on it to evaluate the competitive performance of the proposed model. Meanwhile, extensive visualizations are generated to illustrate the mechanism of our model. Furthermore, the temporal frequency of S2 and GF1-ARD is thoroughly assessed to demonstrate that bandpass alignment can significantly improve the temporal resolution of GF1-ARD. Full article

(This article belongs to the Special Issue Gaofen 16m Analysis Ready Data)

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26 pages, 12137 KiB

Open AccessArticle

GF-1/6 Satellite Pixel-by-Pixel Quality Tagging Algorithm

by Xin Fan, Hao Chang, Lianzhi Huo and Changmiao Hu

Remote Sens. 2023, 15(7), 1955; https://doi.org/10.3390/rs15071955 - 6 Apr 2023

Cited by 1 | Viewed by 1429

Abstract

The Landsat and Sentinel series satellites contain their own quality tagging data products, marking the source image pixel by pixel with several specific semantic categories. These data products generally contain categories such as cloud, cloud shadow, land, water body, and snow. Due to the lack of mid-wave and thermal infrared bands, the accuracy of traditional cloud detection algorithm is unstable when facing Chinese Gaofen-1/6 (GF-1/6) data. Moreover, it is challenging to distinguish clouds from snow. In order to produce GF-1/6 satellite pixel-by-pixel quality tagging data products, this paper builds a training sample set of more than 100,000 image pairs, primarily using Sentinel-2 satellite data. Then, we adopt the Swin Transformer model with a self-attention mechanism for GF-1/6 satellite image quality tagging. Experiments show that the model’s overall accuracy reaches the level of Fmask v4.6 with more than 10,000 training samples, and the model can distinguish between cloud and snow correctly. Our GF-1/6 quality tagging algorithm can meet the requirements of the “Analysis Ready Data (ARD) Technology Research for Domestic Satellite” project. Full article

(This article belongs to the Special Issue Gaofen 16m Analysis Ready Data)

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19 pages, 4599 KiB

Open AccessArticle

MAFF-HRNet: Multi-Attention Feature Fusion HRNet for Building Segmentation in Remote Sensing Images

by Zhihao Che, Li Shen, Lianzhi Huo, Changmiao Hu, Yanping Wang, Yao Lu and Fukun Bi

Remote Sens. 2023, 15(5), 1382; https://doi.org/10.3390/rs15051382 - 28 Feb 2023

Cited by 7 | Viewed by 2137

Abstract

Built-up areas and buildings are two main targets in remote sensing research; consequently, automatic extraction of built-up areas and buildings has attracted extensive attention. This task is usually difficult because of boundary blur, object occlusion, and intra-class inconsistency. In this paper, we propose the multi-attention feature fusion HRNet, MAFF-HRNet, which can retain more detailed features to achieve accurate semantic segmentation. The design of a pyramidal feature attention (PFA) hierarchy enhances the multilevel semantic representation of the model. In addition, we develop a mixed convolutional attention (MCA) block, which increases the capture range of receptive fields and overcomes the problem of intra-class inconsistency. To alleviate interference due to occlusion, a multiscale attention feature aggregation (MAFA) block is also proposed to enhance the restoration of the final prediction map. Our approach was systematically tested on the WHU (Wuhan University) Building Dataset and the Massachusetts Buildings Dataset. Compared with other advanced semantic segmentation models, our model achieved the best IoU results of 91.69% and 68.32%, respectively. To further evaluate the application significance of the proposed model, we migrated a pretrained model based on the World-Cover Dataset training to the Gaofen 16 m dataset for testing. Quantitative and qualitative experiments show that our model can accurately segment buildings and built-up areas from remote sensing images. Full article

(This article belongs to the Special Issue Gaofen 16m Analysis Ready Data)

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18 pages, 6780 KiB

Open AccessArticle

Towards Edge-Precise Cloud and Shadow Detection on the GaoFen-1 Dataset: A Visual, Comprehensive Investigation

by Libin Jiao, Mocun Zheng, Ping Tang and Zheng Zhang

Remote Sens. 2023, 15(4), 906; https://doi.org/10.3390/rs15040906 - 6 Feb 2023

Cited by 1 | Viewed by 1363

Abstract

Remote sensing images are usually contaminated by opaque cloud and shadow regions when acquired, and therefore cloud and shadow detection arises as one of the essential prerequisites for restoration and prediction of the objects of interest underneath, which are required by further processing and analysis. Cutting-edge, learning-based segmentation techniques, given a well-labeled, sufficient sample set, are significantly developed for such a detection issue and can already achieve region-accurate or even pixel-precise performance. However, it may possibly be problematic to attempt to apply the sophisticated segmentation techniques to label-free datasets in a straightforward way, more specifically, to the remote sensing data generated by the Chinese domestic satellite GaoFen-1. We wish to partially address such a segmentation problem from a practical perspective rather than in a conceptual way. This can be performed by considering a hypothesis that a segmentor, which is sufficiently trained on the well-labeled samples of common bands drawn from a source dataset, can be directly applicable to the custom, band-consistent test cases from a target set. Such a band-consistent hypothesis allows us to present a straightforward solution to the GaoFen-1 segmentation problem by treating the well-labeled Landsat 8 Operational Land Imager dataset as the source and by selecting the fourth, the third, and the second bands, also known as the false-color bands, to construct the band-consistent samples and cases. Furthermore, we attempt to achieve edge-refined segmentation performance on the GaoFen-1 dataset by adopting our prior Refined UNet and v4. We finally verify the effectiveness of the band-consistent hypothesis and the edge-refined approaches by performing a relatively comprehensive investigation, including visual comparisons, ablation experiments regarding bilateral manipulations, explorations of critical hyperparameters within our implementation of the conditional random field, and time consumption in practice. The experiments and corresponding results show that the hypothesis of selecting the false-color bands is effective for cloud and shadow segmentation on the GaoFen-1 data, and that edge-refined segmentation performance of our Refined UNet and v4 can be also achieved. Full article

(This article belongs to the Special Issue Gaofen 16m Analysis Ready Data)

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