Automated High-Resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge

Sun, Xian; Wang, Peijin; Yan, Zhiyuan; Diao, Wenhui; Lü, Xiaonan; Yang, Zhujun; Zhang, Yidan; Xiang, Deliang; Chen, Yan; Guo, Jie; Dang, Bo; Wei, Wei; Xu, Feng; Wang, Cheng; Hänsch, Ronny; Weinmann, Martin; Yokoya, Naoto; Fu, Kun

doi:10.1109/jstars.2021.3106941

Cited by 14 publications

(3 citation statements)

References 61 publications

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“…To validate our approach, we conducted experiments on the 2020 Gaofen challenge water body segmentation dataset [44] and the Landsat River dataset created in this study. Figure 5 shows a sample image and mask from these datasets.…”

Section: Methodsmentioning

confidence: 99%

DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images

et al. 2022

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Water body segmentation is an important tool for the hydrological monitoring of the Earth. With the rapid development of convolutional neural networks, semantic segmentation techniques have been used on remote sensing images to extract water bodies. However, some difficulties need to be overcome to achieve good results in water body segmentation, such as complex background, huge scale, water connectivity, and rough edges. In this study, a water body segmentation model (DUPnet) with dense connectivity and multi-scale pyramidal pools is proposed to rapidly and accurately extract water bodies from Gaofen satellite and Landsat 8 OLI (Operational Land Imager) images. The proposed method includes three parts: (1) a multi-scale spatial pyramid pooling module (MSPP) is introduced to combine shallow and deep features for small water bodies and to compensate for the feature loss caused by the sampling process; (2) dense blocks are used to extract more spatial features to DUPnet’s backbone, increasing feature propagation and reuse; (3) a regression loss function is proposed to train the network to deal with the unbalanced dataset caused by small water bodies. The experimental results show that the F1, MIoU, and FWIoU of DUPnet on the 2020 Gaofen dataset are 97.67%, 88.17%, and 93.52%, respectively, and on the Landsat River dataset, they are 96.52%, 84.72%, 91.77%, respectively.

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Section: Methodsmentioning

confidence: 99%

DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images

et al. 2022

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“…GF2020 dataset: The GF2020 dataset (https://drive.google.com/file/d/1oHyzNfHe_ F3MeeUQUoni9dh1LFI_N6RS/view?usp=sharing (accessed on 12 December 2023)) [48] is a high-resolution optical dataset from the GF-2 satellite for Track 5 (automatic water-body segmentation in optical satellite images) from the 2020 Gaofen Challenge. The dataset consists of images with three RGB channels and corresponding ground-truth labels.…”

Section: Wipi Multi-modality Datasetmentioning

confidence: 99%

A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing

Wang,

Zhang,

Chen

et al. 2024

Remote Sensing

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Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset can enhance the water information while reducing the data dimensionality: specifically, the Cloud-Free Label provided in the dataset can effectively alleviate the problem of labeled sample scarcity. Since a single form or uniform kernel size cannot handle the variety of sizes and shapes of water bodies, we propose the Gated Multi-Filter Inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize an attention mechanism by introducing a Gated Channel Transform (GCT) skip connection and integrating GCT into GMF-Inception to further improve model performance. Extensive experiments on three benchmarks, including the WIPI, Chengdu and GF2020 datasets, demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on the WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 scores of 0.9191, 0.7410 and 0.8421, respectively, with the average F1 score on the three datasets 0.0045 higher than that of the U-Net model; likewise, GFLOPS were reduced by 62% on average. The new WIPI dataset, the code and the trained models have been released on GitHub.

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“…At the same time, it contains rich spatial information and spectral information. With the improvement of sensor technology, high-resolution image has attracted more and more attention [1], [2]. In recent years, Airborne Hyperspectral image (HSI) technology has gradually matured, and the resolution of HSI has been significantly improved.…”

Section: Introductionmentioning

confidence: 99%

Dual-Concentrated Network With Morphological Features for Tree Species Classification Using Hyperspectral Image

Guo

Zhang

Jia

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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At present, deep learning is a hot topic in the field of the classification of hyperspectral image (HSI), and it has aroused wide attention. However, in fine-grained classification tasks, such as tree species classification, the uncertain spectrum remains the major factor restraining the classification performance. In order to solve the dilemma of forest tree species classification, a Dualconcentrated Network with Morphological Features (DNMF) is proposed. Firstly, mathematical morphology is used to extract the morphological features of HSI. Then, coarse-grained information is extracted from the original hyperspectral data, and finegrained information is extracted from morphological features. After that, both morphological representations and spectral inputs are fed into DNMF, and the overall evaluation index and visual image are obtained. The advantage of DNMF is that it decouples the spatial and spectral information, and a multisource information fusion process is then simulated. Accordingly, DNMF obtains high tree species classification accuracy. In order to verify the superiority of DNMF, we choose Gaofeng Stateowned Forest Farm (GSFF) in Guangxi Province and the Belgium dataset which was collected near the western part of Belgium as the research area. Related experiments demonstrate that the DNMF model achieves clearly better classification performance over other competitive baselines.

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Automated High-Resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge

Cited by 14 publications

References 61 publications

DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images

DUPnet: Water Body Segmentation with Dense Block and Multi-Scale Spatial Pyramid Pooling for Remote Sensing Images

A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing

Dual-Concentrated Network With Morphological Features for Tree Species Classification Using Hyperspectral Image

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