Large-scale Land Cover Classification in GaoFen-2 Satellite Imagery

Many significant applications need land cover information of remote sensing images that are acquired from different areas and times, such as change detection and disaster monitoring. However, it is difficult to find a generic land cover classification scheme for different remote sensing images due to the spectral shift caused by diverse acquisition condition. In this paper, we develop a novel land cover classification method that can deal with large-scale data captured from widely distributed areas and different times. Additionally, we establish a large-scale land cover classification dataset consisting of 150 Gaofen-2 imageries as data support for model training and performance evaluation. Our experiments achieve outstanding classification accuracy compared with traditional methods.Comment: IGARSS'18 conference pape

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

In this article, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically cosponsored by the IEEE Geoscience and Remote Sensing Society and the International Society for Photogrammetry and Remote Sensing. It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep-learning-based methods have achieved good performance on image interpretation. In this article, we report the details and the best-performing methods presented so far in the scope of this challenge

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Texture Extraction Techniques for the Classification of Vegetation Species in Hyperspectral Imagery: Bag of Words Approach Based on Superpixels

Texture information allows characterizing the regions of interest in a scene. It refers to the spatial organization of the fundamental microstructures in natural images. Texture extraction has been a challenging problem in the field of image processing for decades. In this paper, different techniques based on the classic Bag of Words (BoW) approach for solving the texture extraction problem in the case of hyperspectral images of the Earth surface are proposed. In all cases the texture extraction is performed inside regions of the scene called superpixels and the algorithms profit from the information available in all the bands of the image. The main contribution is the use of superpixel segmentation to obtain irregular patches from the images prior to texture extraction. Texture descriptors are extracted from each superpixel. Three schemes for texture extraction are proposed: codebook-based, descriptor-based, and spectral-enhanced descriptor-based. The first one is based on a codebook generator algorithm, while the other two include additional stages of keypoint detection and description. The evaluation is performed by analyzing the results of a supervised classification using Support Vector Machines (SVM), Random Forest (RF), and Extreme Learning Machines (ELM) after the texture extraction. The results show that the extraction of textures inside superpixels increases the accuracy of the obtained classification map. The proposed techniques are analyzed over different multi and hyperspectral datasets focusing on vegetation species identification. The best classification results for each image in terms of Overall Accuracy (OA) range from 81.07% to 93.77% for images taken at a river area in Galicia (Spain), and from 79.63% to 95.79% for a vast rural region in China with reasonable computation timesThis work was supported in part by the Civil Program UAVs Initiative, promoted by the Xunta de Galicia and developed in partnership with the Babcock Company to promote the use of unmanned technologies in civil services. We also have to acknowledge the support by Ministerio de Ciencia e Innovación, Government of Spain (grant number PID2019-104834GB-I00), and Consellería de Educación, Universidade e Formación Profesional (ED431C 2018/19, and accreditation 2019-2022 ED431G-2019/04). All are cofunded by the European Regional Development Fund (ERDF)S

Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery

The wide field of view (WFV) imaging system onboard the Chinese GaoFen-1 (GF-1) optical satellite has a 16-m resolution and four-day revisit cycle for large-scale Earth observation. The advantages of the high temporal-spatial resolution and the wide field of view make the GF-1 WFV imagery very popular. However, cloud cover is an inevitable problem in GF-1 WFV imagery, which influences its precise application. Accurate cloud and cloud shadow detection in GF-1 WFV imagery is quite difficult due to the fact that there are only three visible bands and one near-infrared band. In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery. The MFC algorithm first implements threshold segmentation based on the spectral features and mask refinement based on guided filtering to generate a preliminary cloud mask. The geometric features are then used in combination with the texture features to improve the cloud detection results and produce the final cloud mask. Finally, the cloud shadow mask can be acquired by means of the cloud and shadow matching and follow-up correction process. The method was validated using 108 globally distributed scenes. The results indicate that MFC performs well under most conditions, and the average overall accuracy of MFC cloud detection is as high as 96.8%. In the contrastive analysis with the official provided cloud fractions, MFC shows a significant improvement in cloud fraction estimation, and achieves a high accuracy for the cloud and cloud shadow detection in the GF-1 WFV imagery with fewer spectral bands. The proposed method could be used as a preprocessing step in the future to monitor land-cover change, and it could also be easily extended to other optical satellite imagery which has a similar spectral setting.Comment: This manuscript has been accepted for publication in Remote Sensing of Environment, vol. 191, pp.342-358, 2017. (http://www.sciencedirect.com/science/article/pii/S003442571730038X

Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation

Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management. However, the dense distribution and various sizes of aquacultures make it challenging to accurately extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric decomposition algorithms were applied to extract 13 polarimetric scattering features. Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index. Subsequently, to reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground functions, the results of which were then used in the marker-controlled watershed algorithm to obtain refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers into three categories (aquaculture, background and dikes) was applied to the marker function. In addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a morphological operation that was conducted for label augmentation. An experimental investigation performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were introduced, our proposed method performed better than standard UNet++ in aquaculture extraction, achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The experimental results indicate that the proposed method can handle the adhesion of both adjacent objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries