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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ke, Sun Shen, Xuhui Lei, Yang China Earthquake Adm Inst Earthquake Sci Beijing 100036 Peoples R China

ISBN: (纸本)9781467311595

In this paper, we introduce some external parameters to attempt to build a suitable standard and to increase automation for quality evaluation of the CBERS-02B high spatial resolution image. The image quality of CBERS-02B was compared with SPOT-5 and P5 satellite in two ways of subjective evaluation and external evaluation. The subjective results show that the quality of SPOT-5 is better than CBERS-02B and P5, and the quality of CBERS-02B is similar to P5 for most land-cover types. The external evaluation gives us quantitative image quality. From the results, the subjective evaluation and the external evaluation are matched pretty well. The parameters introduced in this work are useful to quantitatively evaluate the image quality.

关键词： Quality evaluation CBERS-02B high spatial resolution image

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Estimating Fractional Vegetation Cover of Row Crops from high spatial resolution image

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REMOTE SENSING 2021年第19期13卷

作者： Ma, Xu Lu, Lei Ding, Jianli Zhang, Fei He, Baozhong Xingjiang Univ Coll Resource & Environm Sci Key Lab Oasis Ecol Minist Educ Urumqi 830064 Peoples R China Lanzhou Univ Coll Earth & Environm Sci Lanzhou 730000 Peoples R China

With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5-0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.

关键词： high spatial resolution image fractional vegetation cover row crops neural networks

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EMMCNN: An ETPS-Based Multi-Scale and Multi-Feature Method Using CNN for high spatial resolution image Land-Cover Classification

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REMOTE SENSING 2020年第1期12卷

作者： Zhang, Shuyu Li, Chuanrong Qiu, Shi Gao, Caixia Zhang, Feng Du, Zhenhong Liu, Renyi Zhejiang Univ Sch Earth Sci Hangzhou 310027 Peoples R China Chinese Acad Sci Acad Optoelect Key Lab Quantitat Remote Sensing Informat Technol Beijing 100094 Peoples R China Zhejiang Prov Key Lab Geog Informat Sci Hangzhou 310028 Peoples R China

Land-cover information is significant for land-use planning, urban management, and environment monitoring. This paper presented a novel extended topology-preserving segmentation (ETPS)-based multi-scale and multi-feature method using the convolutional neural network (EMMCNN) for high spatial resolution (HSR) image land-cover classification. The EMMCNN first segmented the images into superpixels using the ETPS algorithm with false-color composition and enhancement and built parallel convolutional neural networks (CNNs) with dense connections for superpixel multi-scale deep feature learning. Then, the multi-resolution segmentation (MRS) object hand-delineated features were extracted and mapped to superpixels for complementary multi-segmentation and multi-type representation. Finally, a hybrid network was designed to consist of 1-dimension CNN and multi-layer perception (MLP) with channel-wise stacking and attention-based weighting for adaptive feature fusion and comprehensive classification. Experimental results on four real HSR GaoFen-2 datasets demonstrated the superiority of the proposed EMMCNN over several well-known classification methods in terms of accuracy and consistency, with overall accuracy averagely improved by 1.74% to 19.35% for testing images and 1.06% to 8.78% for validating images. It was found that the solution combining an appropriate number of larger scales and multi-type features is recommended for better performance. Efficient superpixel segmentation, networks with strong learning ability, optimized multi-scale and multi-feature solution, and adaptive attention-based feature fusion were key points for improving HSR image land-cover classification in this study.

关键词： attention-based weighting convolutional neural network high spatial resolution image land-cover classification multi-scale and multi-feature fusion superpixel segmentation

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Study on terrain radiant correction of high spatial resolution image of mountainous area

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Zhejiang Daxue Xuebao (Nongye yu Shengming Kexue Ban)/Journal of the Zhejiang University - Agriculture and Life Science 2006年第6期32卷 693-698页

作者： Zhang, Chao Wang, Ren-Chao Zhao, Chun-Jiang Li, Hai-Xia Liu, Liang-Yun College of Information and Electrical Engineering China Agricultural University Beijing 100083 China Institute of Agriculture Remote sensing and Information Application Zhejiang University Hangzhou 310029 China National Engineering Research Center for Information Technology in Agriculture Beijing 100089 China

high spatial resolution remote sensing images such as satellite image and airborne image are much widely used in agriculture, forestry recourses inventory and the mountain ecology monitoring area. However, the shades in image caused by terrain and the sensor side view scanning impact the application of these images. To this problem, given the moment matching theory which is used successfully in destriping the remote sensing image, the terrain radiant correction based on moment matching was presented. The C correction method which is validated the best terrain correction method to TM image was improved. One meter spatial resolution airborne image of Beijing west mountainous area was corrected by moment matching and C correction improved method. Then the processed results were compared and analyzed. Results show that the moment matching is better than the C correction improved method to the terrain complex area and large image.

关键词： C correction method high spatial resolution image Moment matching Remote sensing Terrain radiant correction

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Purifying SLIC Superpixels to Optimize Superpixel-Based Classification of high spatial resolution Remote Sensing image

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REMOTE SENSING 2019年第22期11卷

作者： Tong, Hengjian Tong, Fei Zhou, Wei Zhang, Yun China Univ Geosci Sch Comp Sci Lumo Rd 388 Wuhan 430074 Hubei Peoples R China Univ New Brunswick CRC Lab Adv Geomat Image Proc Dept Geodesy & Geomat Engn 15 Dineen Dr Fredericton NB E3B 5A3 Canada

Fast and accurate classification of high spatial resolution remote sensing image is important for many applications. The usage of superpixels in classification has been proposed to accelerate the speed of classification. However, although most superpixels only contain pixels from single class, there are still some mixed superpixels, which mostly locate near the edge of different classes, and contain pixels from more than one class. Such mixed superpixels will cause misclassification regardless of classification methods used. In this paper, a superpixels purification algorithm based on color quantization is proposed to purify mixed Simple Linear Iterative Clustering (SLIC) superpixels. After purifying, the mixed SLIC superpixel will be separated into smaller superpixels. These smaller superpixels are pure superpixels which only contain a single kind of ground object. The experiments on images from the dataset BSDS500 show that the purified SLIC superpixels outperform the original SLIC superpixels on three segmentation evaluation metrics. With the purified SLIC superpixels, a classification scheme in which only edge superpixels are selected to be purified is proposed. The strategy of purifying edge superpixels not only improves the efficiency of the algorithm, but also improves the accuracy of the classification. The experiments on a remote sensing image from WorldView-2 satellite demonstrate that purified SLIC superpixels at all scales can generate classification result with higher accuracy than original SLIC superpixels, especially at the scale of 20 x 20, for which the accuracy increase is higher than 4%.

关键词： classification high spatial resolution image purified superpixel edge superpixels SLIC color quantization

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Exploiting Superpixel-Based Contextual Information on Active Learning for high spatial resolution Remote Sensing image Classification

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REMOTE SENSING 2023年第3期15卷

作者： Tang, Jiechen Tong, Hengjian Tong, Fei Zhang, Yun Chen, Weitao China Univ Geosci Sch Comp Sci 68 Jincheng St East Lake New Technol Dev Zone Wuhan 430078 Peoples R China Univ New Brunswick Dept Geodesy & Geomatics Engn 15 Dineen Dr Fredericton NB E3B 5A3 Canada

Superpixel-based classification using Active Learning (AL) has shown great potential in high spatial resolution remote sensing image classification tasks. However, in existing superpixel-based classification models using AL, the expert labeling information is only used on the selected informative superpixel while its neighboring superpixels are ignored. Actually, as most superpixels are over-segmented, a ground object always contains multiple superpixels. Thus, the center superpixel tends to have the same label as its neighboring superpixels. In this paper, to make full use of the expert labeling information, a Similar Neighboring Superpixels Search and Labeling (SNSSL) method was proposed and used in the AL process. Firstly, we identify superpixels with certain categories and uncertain superpixels by supervised learning. Secondly, we use the active learning method to process those uncertain superpixels. In each round of AL, the expert labeling information is not only used to enrich the training set but also used to label the similar neighboring superpixels. Similar neighboring superpixels are determined by computing the similarity of two superpixels according to CIELAB Dominant Colors distance, Correlation distance, Angular Second Moment distance and Contrast distance. The final classification map is composed of the supervised learning classification map and the active learning with SNSSL classification map. To demonstrate the performance of the proposed SNSSL method, the experiments were conducted on images from two benchmark high spatial resolution remote sensing datasets. The experiment shows that overall accuracy, average accuracy and kappa coefficients of the classification using the SNSSL have been improved obviously compared with the classification without the SNSSL.

关键词： high spatial resolution image superpixel-based image classification active learning supervised learning label spread

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high-resolution feature pyramid attention network for high spatial resolution images land-cover classification in arid oasis zones

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第11期45卷 3664-3688页

作者： Chen, Pengdi Liu, Yong Liu, Yi Ren, Yuanrui Zhang, Baoan Gao, Xiaolong Lanzhou Univ Coll Earth & Environm Sci Lanzhou Peoples R China Mapping Inst Gansu Prov Dept Remote Sensing & Geog Natl Condit Monitoring Lanzhou Peoples R China

Land-cover classification based on remote sensing technology has been adopted for decision-making concerning agricultural development, urban planning, and ecosystem protection in arid oasis zones. The semantic segmentation method based on deep learning, as a new paradigm, can effectively overcome the limitations of traditional pixel-based and object-based methods and obtain good classification results from high spatial resolution (HSR) remote sensing images. However, how to extract the exact category boundary and realize the high precision mapping is still a problem. This paper proposes a novel high-resolution feature pyramid attention network (HRFPANet) for land-cover classification. It effectively integrates the advantages of multi-scale feature extraction, attention mechanism, and feature fusion and alleviates boundary inconsistency, roughness, and category fragmentation associated with previous semantic segmentation models. The experimental results show that the mIoU score of HRFPANet is 79.5%, which is 11.5% and 2.6% higher than that of PSPNet and UPerNet, respectively. It proves the proposed model can be used for qualified land-cover mapping in arid oasis zones. Our source code is available at https://***/HPU-CPD/***.

关键词： Arid oasis zones high spatial resolution image multi-scale semantic segmentation land-cover classification

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Multiagent Object-Based Classifier for high spatial resolution imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2014年第2期52卷 841-857页

作者： Zhong, Yanfei Zhao, Bei Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

Object-based classification, including object-based segmentation and classification, has been applied for the classification of high spatial resolution imagery due to the increase in the spatial resolution and the limited spectral resolution. Because of the independent design of the object-based segmentation and classification in many of the traditional object-based classification methods, additional work is required to select the appropriate segmentation algorithms to match the classification algorithms. The object-based segmentation algorithms, e. g., the fractal net evolution approach (FNEA), have been successfully utilized to provide the homogeneous regions, and are the basis of object-based classification. However, the traditional FNEA algorithm is greatly influenced by the global control strategy of the region-growing procedure. In addition, the existing object classification methods take little account of the object context information, which is important for high spatial-resolution image interpretation. To improve the accuracy of the object-based classification, in this paper, a multiagent object-based classification framework (MAOCF) for high-resolution remote sensing imagery is proposed. The proposed approach avoids the issue of segmentation algorithm selection by unifying the processing of object-based segmentation and classification through the use of a 4-tuple agent model. In the uniform framework, a multiagent object-based segmentation (MAOS) algorithm is proposed to optimally control the procedure of object merging. In addition, a MAOC is proposed to utilize the contextual information from the surrounding objects by taking advantage of the benefits of a multiagent system, e. g., strong interaction, high flexibility, and parallel global control capability. Due to the characteristics of a multiagent system, MAOCF has the potential for a parallel computing ability. Three experiments with different types of images were performed to evaluate the performanc

关键词： Global control high spatial resolution image multiagent systems object-based classification segmentation

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Multi-scale Landscape Pattern Analysis Based on high spatial resolution imagery Segmentation

Multi-scale Landscape Pattern Analysis Based on High Spatial...

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21st International Conference on Geoinformatics (Geoinformatics)

作者： Yang Yetao Wang Yingying China Univ Geosci Inst Geophys & Geomat Beijing Peoples R China Hubei Provincial Bur Meteorol Huangshi Peoples R China

ISBN: (纸本)9781467362269

The development of high spatial resolution remote sensing, such as IKONOS and QuickBird, makes it possible to describe the landscape pattern in the smaller scales. However, the level of landscape pattern detail represented by high spatial resolution images is different from middle and low resolution images. The image process and image analysis methods are also different as well. New techniques including image segmentation and object-based analysis are widely used in the high spatial resolution imagery analysis. So the landscape models and analysis methods set up by middle and low spatial resolution remote sensing are not suitable for the high spatial resolution remote sensing. In this study, focusing on the method for landscape pattern analysis for high spatial resolution remote sensing, we try to achieve the following goals: 1) set up a hybrid landscape model to describe the landscape patch mosaic, continuum and connectivity characteristics based on a unified data structure;2) set up a landscape scaling method based on high spatial resolution image object-oriented analysis. The result shows that Delaunay-Voronoi data structure based on image object-oriented analysis is proper to set up the hybrid landscape model. Multi-scale image segmentation is better than aggregation method for multi-scale landscape pattern analysis.

关键词： landscape pattern OBIA multi-scale high spatial resolution image

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Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale high spatial resolution images

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REMOTE SENSING 2021年第23期13卷

作者： Yu, Bo Chen, Fang Wang, Yu Wang, Ning Yang, Xiaoyu Ma, Pengfei Zhou, Chunyan Zhang, Yuhuan Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Hainan Key Lab Earth Observat Sanya 572029 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing 100094 Peoples R China Minist Ecol & Environm Ctr Satellite Applicat Ecol & Environm Beijing 100006 Peoples R China Jinan Environm Monitoring Ctr Shandong Prov Jinan 250013 Peoples R China

Oil tank inventory is significant for the economy and the military, as it can be used to estimate oil reserves. Traditional oil tank detection methods mainly focus on the geometrical characteristics and spectral features of remotely sensed images based on feature engineering. The methods have a limited application capability when the distribution pattern of ground objects in the image changes and the imaging condition varies largely. Therefore, we propose an end-to-end deep convolution network Res2-Unet+, to detect oil tanks in a large-scale area. The Res2-Unet+ method replaces the typical convolution block in the encoder of the original Unet method using hierarchical residual learning branches. A hierarchical branch is used to decompose the feature map into a few sub-channel features. To evaluate the generalization and transferability of the proposed model, we use high spatial resolution images from three different sensors in different areas to train the oil tank detection model. images from yet another sensor in another area are used to evaluate the trained model. Three more widely used methods, Unet, Segnet, and PSPNet, are trained and evaluated for the same dataset. The experiments prove the effectiveness, strong generalization, and transferability of the proposed Res2-Unet+ method.

关键词： oil tank detection high spatial resolution image oil reservation

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