scholarly journals A new privacy attack network for remote sensing images classification with small training samples

2019 ◽  
Vol 16 (5) ◽  
pp. 4456-4476 ◽  
Author(s):  
Eric Ke Wang ◽  
◽  
Fan Wang ◽  
Ruipei Sun ◽  
Xi Liu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Images ◽  
Training Samples ◽  
Privacy Attack

scholarly journals An Improved Segmentation Method for Automatic Mapping of Cone Karst from Remote Sensing Data Based on DeepLab V3+ Model

2021 ◽  
Vol 13 (3) ◽  
pp. 441
Author(s):  
Han Fu ◽  
Bihong Fu ◽  
Pilong Shi
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Guizhou Province ◽  
Training Dataset ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Karst Landscape ◽  
Heritage Site ◽  
Training Samples ◽  
Input Module

The South China Karst, a United Nations Educational, Scientific and Cultural Organization (UNESCO) natural heritage site, is one of the world’s most spectacular examples of humid tropical to subtropical karst landscapes. The Libo cone karst in the southern Guizhou Province is considered as the world reference site for these types of karst, forming a distinctive and beautiful landscape. Geomorphic information and spatial distribution of cone karst is essential for conservation and management for Libo heritage site. In this study, a deep learning (DL) method based on DeepLab V3+ network was proposed to document the cone karst landscape in Libo by multi-source data, including optical remote sensing images and digital elevation model (DEM) data. The training samples were generated by using Landsat remote sensing images and their combination with satellite derived DEM data. Each group of training dataset contains 898 samples. The input module of DeepLab V3+ network was improved to accept four-channel input data, i.e., combination of Landsat RGB images and DEM data. Our results suggest that the mean intersection over union (MIoU) using the four-channel data as training samples by a new DL-based pixel-level image segmentation approach is the highest, which can reach 95.5%. The proposed method can accomplish automatic extraction of cone karst landscape by self-learning of deep neural network, and therefore it can also provide a powerful and automatic tool for documenting other type of geological landscapes worldwide.

scholarly journals Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification

2018 ◽  
Vol 10 (12) ◽  
pp. 1934 ◽  
Author(s):  
Bao-Di Liu ◽  
Wen-Yang Xie ◽  
Jie Meng ◽  
Ye Li ◽  
Yanjiang Wang
Keyword(s):  
Remote Sensing ◽  
Test Sample ◽  
Remote Sensing Image ◽  
Image Features ◽  
Superior Performance ◽  
Collaborative Representation ◽  
Great Success ◽  
Specific Class ◽  
Remote Sensing Images ◽  
Training Samples

In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.

scholarly journals Neural network technology to search for targets in remote sensing images of the Earth

2019 ◽  
pp. 180-186
Author(s):  
N S Abramov ◽  
А А Talalayev ◽  
V P Fralenko ◽  
O G Shishkin ◽  
V M Khachumov
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
High Performance ◽  
Remote Sensing Images ◽  
Single Class ◽  
The Earth ◽  
Training Samples ◽  
Performance Computing ◽  
Neural Network Technology

The paper introduces how multi-class and single-class problems of searching and classifying target objects in remote sensing images of the Earth are solved. To improve the recognition efficiency, the preparation tools for training samples, optimal configuration and use of deep learning neural networks using high-performance computing technologies have been developed. Two types of CNN were used to process ERS images: a convolutional neural network from the nnForge library and a network of the Darknet type. A comparative analysis of the results is obtained. The research showed that the capabilities of convolutional neural networks allow solving simultaneously the problems of searching (localizing) and recognizing objects in ERS images with high accuracy and completeness.

scholarly journals APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF HIGH RESOLUTION AGRICULTURAL REMOTE SENSING IMAGES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 989-992 ◽  
Author(s):  
C. Yao ◽  
Y. Zhang ◽  
Y. Zhang ◽  
H. Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Precision Agriculture ◽  
Rapid Development ◽  
Reference Value ◽  
Remote Sensing Images ◽  
Training Samples ◽  
Traditional Classification ◽  
Crop Classification

With the rapid development of Precision Agriculture (PA) promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN). For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.

scholarly journals Remote Sensing Image Augmentation Based on Text Description for Waterside Change Detection

2021 ◽  
Vol 13 (10) ◽  
pp. 1894
Author(s):  
Chen Chen ◽  
Hongxiang Ma ◽  
Guorun Yao ◽  
Ning Lv ◽  
Hua Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Change Detection ◽  
Data Augmentation ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Administrative Procedure ◽  
Adversarial Network ◽  
Training Samples ◽  
Perlin Noise

Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, “Inception Score”, “Human Rank”, and “Inference Time” are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model.

scholarly journals APPLICATION OF MACHINE LEARNING IN URBAN GREENERY LAND COVER EXTRACTION

2018 ◽  
Vol XLII-3 ◽  
pp. 1409-1413 ◽  
Author(s):  
X. Qiao ◽  
L. L. Li ◽  
D. Li ◽  
Y. L. Gan ◽  
A. Y. Hou
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Land Resource ◽  
Remote Sensing Images ◽  
Large Area ◽  
Urban Greenery ◽  
Training Samples ◽  
Unlabelled Data ◽  
Multispectral Remote Sensing ◽  
Trained Neural Network

Urban greenery is a critical part of the modern city and the greenery coverage information is essential for land resource management, environmental monitoring and urban planning. It is a challenging work to extract the urban greenery information from remote sensing image as the trees and grassland are mixed with city built-ups. In this paper, we propose a new automatic pixel-based greenery extraction method using multispectral remote sensing images. The method includes three main steps. First, a small part of the images is manually interpreted to provide prior knowledge. Secondly, a five-layer neural network is trained and optimised with the manual extraction results, which are divided to serve as training samples, verification samples and testing samples. Lastly, the well-trained neural network will be applied to the unlabelled data to perform the greenery extraction. The GF-2 and GJ-1 high resolution multispectral remote sensing images were used to extract greenery coverage information in the built-up areas of city X. It shows a favourable performance in the 619 square kilometers areas. Also, when comparing with the traditional NDVI method, the proposed method gives a more accurate delineation of the greenery region. Due to the advantage of low computational load and high accuracy, it has a great potential for large area greenery auto extraction, which saves a lot of manpower and resources.

scholarly journals Gated Convolutional Networks for Cloud Removal From Bi-Temporal Remote Sensing Images

2020 ◽  
Vol 12 (20) ◽  
pp. 3427 ◽  
Author(s):  
Peiyu Dai ◽  
Shunping Ji ◽  
Yongjun Zhang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Deep Learning ◽  
Total Variation ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Training Samples ◽  
Global Consistency ◽  
Cloud Removal ◽  
First Time

Pixels of clouds and cloud shadows in a remote sensing image impact image quality, image interpretation, and subsequent applications. In this paper, we propose a novel cloud removal method based on deep learning that automatically reconstructs the invalid pixels with the auxiliary information from multi-temporal images. Our method’s innovation lies in its feature extraction and loss functions, which reside in a novel gated convolutional network (GCN) instead of a series of common convolutions. It takes the current cloudy image, a recent cloudless image, and the mask of clouds as input, without any requirements of external training samples, to realize a self-training process with clean pixels in the bi-temporal images as natural training samples. In our feature extraction, gated convolutional layers, for the first time, are introduced to discriminate cloudy pixels from clean pixels, which make up for a common convolution layer’s lack of the ability to discriminate. Our multi-level constrained joint loss function, which consists of an image-level loss, a feature-level loss, and a total variation loss, can achieve local and global consistency both in shallow and deep levels of features. The total variation loss is introduced into the deep-learning-based cloud removal task for the first time to eliminate the color and texture discontinuity around cloud outlines needing repair. On the WHU cloud dataset with diverse land cover scenes and different imaging conditions, our experimental results demonstrated that our method consistently reconstructed the cloud and cloud shadow pixels in various remote sensing images and outperformed several mainstream deep-learning-based methods and a conventional method for every indicator by a large margin.

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