Farmland segmentation from remote sensing images using deep learning methods

2020 ◽  
Author(s):  
Wei Sun ◽  
Rong Zhou ◽  
Congcong Nie ◽  
Liuan Wang ◽  
Jun Sun
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Remote Sensing Images ◽  
Learning Methods

Semantic segmentation of remote sensing images based on deep learning methods

2021 ◽  
Author(s):  
Cong Huang ◽  
Yao Yang ◽  
Huajun Wang ◽  
Yu Ma ◽  
Jinquan Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Learning Methods

scholarly journals A novel approach for scene classification from remote sensing images using deep learning methods

2020 ◽  
pp. 1-13
Author(s):  
Xiaowei Xu ◽  
Yinrong Chen ◽  
Junfeng Zhang ◽  
Yu Chen ◽  
Prathik Anandhan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Learning Methods ◽  
Novel Approach

scholarly journals Target detection from high-resolution remote sensing images using deep learning methods

2019 ◽  
Vol 11 (1) ◽  
pp. 48-64
Author(s):  
نیما فرهادی ◽  
عباس کیانی ◽  
حمید عبادی
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Target Detection ◽  
Remote Sensing Images ◽  
Learning Methods

scholarly journals Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

2021 ◽  
Vol 13 (21) ◽  
pp. 4443
Author(s):  
Bo Jiang ◽  
Guanting Chen ◽  
Jinshuai Wang ◽  
Hang Ma ◽  
Lin Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Superior Performance ◽  
Learning Technology ◽  
Remote Sensing Images ◽  
Learning Methods ◽  
Atmospheric Scattering

The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder–decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively.

scholarly journals Deep Learning Based Electric Pylon Detection in Remote Sensing Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1857
Author(s):  
Sijia Qiao ◽  
Yu Sun ◽  
Haopeng Zhang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Detection Methods ◽  
Normal Operation ◽  
Remote Sensing Images ◽  
Learning Methods ◽  
Low Efficiency ◽  
Different Characteristics ◽  
Deep Learning Model ◽  
Selection Of

The working condition of power network can significantly influence urban development. Among all the power facilities, electric pylon has an important effect on the normal operation of electricity supply. Therefore, the work status of electric pylons requires continuous and real-time monitoring. Considering the low efficiency of manual detection, we propose to utilize deep learning methods for electric pylon detection in high-resolution remote sensing images in this paper. To verify the effectiveness of electric pylon detection methods based on deep learning, we tested and compared the comprehensive performance of 10 state-of-the-art deep-learning-based detectors with different characteristics. Extensive experiments were carried out on a self-made dataset containing 1500 images. Moreover, 50 relatively complicated images were selected from the dataset to test and evaluate the adaptability to actual complex situations and resolution variations. Experimental results show the feasibility of applying deep learning methods to electric pylon detection. The comparative analysis can provide reference for the selection of specific deep learning model in actual electric pylon detection task.

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 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 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.

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