scholarly journals AAU-Net: Attention-Based Asymmetric U-Net for Subject-Sensitive Hashing of Remote Sensing Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5109
Author(s):  
Kaimeng Ding ◽  
Shiping Chen ◽  
Yu Wang ◽  
Yueming Liu ◽  
Yue Zeng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Asymmetric Structure ◽  
Learning Models ◽  
Remote Sensing Images ◽  
Structure Characteristics ◽  
Special Subset ◽  
The Subject ◽  
Perceptual Hashing ◽  
Hashing Algorithm

The prerequisite for the use of remote sensing images is that their security must be guaranteed. As a special subset of perceptual hashing, subject-sensitive hashing overcomes the shortcomings of the existing perceptual hashing that cannot distinguish between “subject-related tampering” and “subject-unrelated tampering” of remote sensing images. However, the existing subject-sensitive hashing still has a large deficiency in robustness. In this paper, we propose a novel attention-based asymmetric U-Net (AAU-Net) for the subject-sensitive hashing of remote sensing (RS) images. Our AAU-Net demonstrates obvious asymmetric structure characteristics, which is important to improve the robustness of features by combining the attention mechanism and the characteristics of subject-sensitive hashing. On the basis of AAU-Net, a subject-sensitive hashing algorithm is developed to integrate the features of various bands of RS images. Our experimental results show that our AAU-Net-based subject-sensitive hashing algorithm is more robust than the existing deep learning models such as Attention U-Net and MUM-Net, and its tampering sensitivity remains at the same level as that of Attention U-Net and MUM-Net.

scholarly journals Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

2021 ◽  
Vol 13 (13) ◽  
pp. 2524
Author(s):  
Ziyi Chen ◽  
Dilong Li ◽  
Wentao Fan ◽  
Haiyan Guan ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Extraction Methods ◽  
The Self ◽  
Optical Remote Sensing ◽  
Building Extraction ◽  
Learning Models ◽  
Remote Sensing Images ◽  
Segmentation Methods

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

scholarly journals An Oil Well Dataset Derived from Satellite-Based Remote Sensing

2021 ◽  
Vol 13 (6) ◽  
pp. 1132
Author(s):  
Zhibao Wang ◽  
Lu Bai ◽  
Guangfu Song ◽  
Jie Zhang ◽  
Jinhua Tao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
State Of The Art ◽  
Google Earth ◽  
Oil Wells ◽  
Oil Well ◽  
Optical Remote Sensing ◽  
Learning Models ◽  
Remote Sensing Images

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

2018 ◽  
Vol 10 (6) ◽  
pp. 964 ◽  
Author(s):  
Zhenfeng Shao ◽  
Ke Yang ◽  
Weixun Zhou
Keyword(s):  
Remote Sensing ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Image Retrieval ◽  
Semantic Segmentation ◽  
Semantic Content ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Benchmark Datasets ◽  
Feature Based

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.

scholarly journals Deep Learning Based Fossil-Fuel Power Plant Monitoring in High Resolution Remote Sensing Images: A Comparative Study

2019 ◽  
Vol 11 (9) ◽  
pp. 1117 ◽  
Author(s):  
Haopeng Zhang ◽  
Qin Deng
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Power Plants ◽  
North China ◽  
Fossil Fuel ◽  
Environment Monitoring ◽  
Remote Sensing Images ◽  
Plant Monitoring ◽  
Working Status ◽  
Fossil Fuel Power Plants

The frequent hazy weather with air pollution in North China has aroused wide attention in the past few years. One of the most important pollution resource is the anthropogenic emission by fossil-fuel power plants. To relieve the pollution and assist urban environment monitoring, it is necessary to continuously monitor the working status of power plants. Satellite or airborne remote sensing provides high quality data for such tasks. In this paper, we design a power plant monitoring framework based on deep learning to automatically detect the power plants and determine their working status in high resolution remote sensing images (RSIs). To this end, we collected a dataset named BUAA-FFPP60 containing RSIs of over 60 fossil-fuel power plants in the Beijing-Tianjin-Hebei region in North China, which covers about 123 km 2 of an urban area. We compared eight state-of-the-art deep learning models and comprehensively analyzed their performance on accuracy, speed, and hardware cost. Experimental results illustrate that our deep learning based framework can effectively detect the fossil-fuel power plants and determine their working status with mean average precision up to 0.8273, showing good potential for urban environment monitoring.

scholarly journals Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning

2017 ◽  
Vol 12 ◽  
pp. 05012 ◽  
Author(s):  
Ying Liu ◽  
Hong-Yuan Cui ◽  
Zheng Kuang ◽  
Guo-Qing Li
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection
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