Remote Sensing Image Mosaic Algorithm

2012 ◽  
Vol 500 ◽  
pp. 716-721
Author(s):  
Yi Ding Wang ◽  
Shuai Qin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Hot Spot ◽  
Remote Sensing Image ◽  
Processing Algorithm ◽  
Geometric Features ◽  
Image Mosaic ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Image Pixels

In the field of remote sensing, the acquirement of higher resolution of remote sensing images has become a hot spot issue with widely use of high resolution of remote sensing images. This paper focus on the characteristics of high resolution remote sensing images, on the basis of fully considerate of the correlation between geometric features and image pixels, bring forward a fusion of image mosaic processing algorithm. With this algorithm, the surface features can be well preserved after the processing of mosaic the remote sensing images, and the overlapping area can transit naturally, it will be better for the post-processing, analysis and application.

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

scholarly journals MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4528
Author(s):  
Xin Yang ◽  
Lei Hu ◽  
Yongmei Zhang ◽  
Yunqing Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Remote Sensing Image ◽  
Image Features ◽  
Remote Sensing Images ◽  
The Rich ◽  
The Difference ◽  
Basic Network ◽  
And Performance

Remote sensing image change detection (CD) is an important task in remote sensing image analysis and is essential for an accurate understanding of changes in the Earth’s surface. The technology of deep learning (DL) is becoming increasingly popular in solving CD tasks for remote sensing images. Most existing CD methods based on DL tend to use ordinary convolutional blocks to extract and compare remote sensing image features, which cannot fully extract the rich features of high-resolution (HR) remote sensing images. In addition, most of the existing methods lack robustness to pseudochange information processing. To overcome the above problems, in this article, we propose a new method, namely MRA-SNet, for CD in remote sensing images. Utilizing the UNet network as the basic network, the method uses the Siamese network to extract the features of bitemporal images in the encoder separately and perform the difference connection to better generate difference maps. Meanwhile, we replace the ordinary convolution blocks with Multi-Res blocks to extract spatial and spectral features of different scales in remote sensing images. Residual connections are used to extract additional detailed features. To better highlight the change region features and suppress the irrelevant region features, we introduced the Attention Gates module before the skip connection between the encoder and the decoder. Experimental results on a public dataset of remote sensing image CD show that our proposed method outperforms other state-of-the-art (SOTA) CD methods in terms of evaluation metrics and performance.

scholarly journals U-net Network for Building Information Extraction of Remote-Sensing Imagery

2018 ◽  
Vol 14 (12) ◽  
pp. 179
Author(s):  
Jingtan Li ◽  
Maolin Xu ◽  
Hongling Xiu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Image Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Building Information ◽  
The Face

With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.

scholarly journals LAM: Remote Sensing Image Captioning with Label-Attention Mechanism

2019 ◽  
Vol 11 (20) ◽  
pp. 2349 ◽  
Author(s):  
Zhengyuan Zhang ◽  
Wenhui Diao ◽  
Wenkai Zhang ◽  
Menglong Yan ◽  
Xin Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Image Features ◽  
Attention Mechanism ◽  
Word Embedding ◽  
Remote Sensing Images ◽  
Image Captioning ◽  
Scoring Method ◽  
Label Information

Significant progress has been made in remote sensing image captioning by encoder-decoder frameworks. The conventional attention mechanism is prevalent in this task but still has some drawbacks. The conventional attention mechanism only uses visual information about the remote sensing images without considering using the label information to guide the calculation of attention masks. To this end, a novel attention mechanism, namely Label-Attention Mechanism (LAM), is proposed in this paper. LAM additionally utilizes the label information of high-resolution remote sensing images to generate natural sentences to describe the given images. It is worth noting that, instead of high-level image features, the predicted categories’ word embedding vectors are adopted to guide the calculation of attention masks. Representing the content of images in the form of word embedding vectors can filter out redundant image features. In addition, it can also preserve pure and useful information for generating complete sentences. The experimental results from UCM-Captions, Sydney-Captions and RSICD demonstrate that LAM can improve the model’s performance for describing high-resolution remote sensing images and obtain better S m scores compared with other methods. S m score is a hybrid scoring method derived from the AI Challenge 2017 scoring method. In addition, the validity of LAM is verified by the experiment of using true labels.

scholarly journals REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA

2016 ◽  
Vol XLI-B7 ◽  
pp. 419-422
Author(s):  
Xin Yu ◽  
Zongyong Wen ◽  
Zhaorong Zhu ◽  
Qiang Xia ◽  
Lan Shun
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
High Resolution ◽  
Bayesian Networks ◽  
Image Classification ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Geographical Information ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

scholarly journals Batch Processing of Remote Sensing Image Mosaic based on Python

2018 ◽  
Vol 14 (09) ◽  
pp. 208
Author(s):  
Hongling Xiu ◽  
Fengyun Yang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Analysis ◽  
Remote Sensing Image ◽  
Image Mosaic ◽  
Remote Sensing Images ◽  
Complicated Process ◽  
Processing Software ◽  
Image Processing Software ◽  
Remote Sensing Image Processing

In the process of remote sensing image processing, analysis and interpretation, it is usually necessary to combine several local images into a complete image. Aiming at the shortcoming of long and complicated process of conventional semi-automatic video stitching. In this paper, using the splicing method of pixels, based on the Python interface of ArcGIS 10.1 platform, the idea of programming language is introduced and batch mosaic of remote sensing images is realized. Through the comparison with the image processing software, it is found that this method can shorten the time of image mosaic and improve the efficiency of splicing, which is convenient for later image analysis and other work under the premise of ensuring the accuracy.

scholarly journals Full Convolutional Neural Network Based on Multi-Scale Feature Fusion for the Class Imbalance Remote Sensing Image Classification

2020 ◽  
Vol 12 (21) ◽  
pp. 3547 ◽  
Author(s):  
Yuanyuan Ren ◽  
Xianfeng Zhang ◽  
Yongjian Ma ◽  
Qiyuan Yang ◽  
Chuanjian Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Spatial Information ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
Machine Learning Algorithms ◽  
Image Feature ◽  
Remote Sensing Images ◽  
Land Covers

Remote sensing image segmentation with samples imbalance is always one of the most important issues. Typically, a high-resolution remote sensing image has the characteristics of high spatial resolution and low spectral resolution, complex large-scale land covers, small class differences for some land covers, vague foreground, and imbalanced distribution of samples. However, traditional machine learning algorithms have limitations in deep image feature extraction and dealing with sample imbalance issue. In the paper, we proposed an improved full-convolution neural network, called DeepLab V3+, with loss function based solution of samples imbalance. In addition, we select Sentinel-2 remote sensing images covering the Yuli County, Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region, China as data sources, then a typical region image dataset is built by data augmentation. The experimental results show that the improved DeepLab V3+ model can not only utilize the spectral information of high-resolution remote sensing images, but also consider its rich spatial information. The classification accuracy of the proposed method on the test dataset reaches 97.97%. The mean Intersection-over-Union reaches 87.74%, and the Kappa coefficient 0.9587. The work provides methodological guidance to sample imbalance correction, and the established data resource can be a reference to further study in the future.

scholarly journals Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification

2020 ◽  
Vol 12 (6) ◽  
pp. 1012 ◽  
Author(s):  
Cheng Shi ◽  
Zhiyong Lv ◽  
Xiuhong Yang ◽  
Pengfei Xu ◽  
Irfana Bibi
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Supervised Learning ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Remote Sensing Images ◽  
Training Set ◽  
Remote Sensing Image Classification ◽  
Very High

Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. Labeled samples are difficult to obtain and costly. Therefore, semi-supervised learning becomes an effective paradigm that combines the labeled and unlabeled samples for classification. In semi-supervised learning, the key issue is to enlarge the training set by selecting highly-reliable unlabeled samples. Observing the samples from multiple views is helpful to improving the accuracy of label prediction for unlabeled samples. Hence, the reasonable view partition is very important for improving the classification performance. In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. Firstly, a superpixel-based sample enlargement method is proposed to increase the number of training samples in each view. Secondly, a view partition method is designed to partition the training set into two independent views, and the partitioned subsets are characterized by being inter-distinctive and intra-compact. Finally, a collaborative classification strategy is proposed for the final classification. Experiments are conducted on three VHR remote sensing images, and the results show that the proposed method performs better than several state-of-the-art methods.

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