Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis
Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic a...
| Main Authors: | , , , , , , |
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| Format: | Article |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/17/3444 |
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| author | Hao Wang Zhendong Ding Xinyi Li Shiyu Shen Xiaodong Ye Dan Zhang Shifei Tao |
| author_facet | Hao Wang Zhendong Ding Xinyi Li Shiyu Shen Xiaodong Ye Dan Zhang Shifei Tao |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) images despeckling. However, these CNN-based SAR images despeckling approaches usually require large computational resources, especially in the case of huge training data. In this paper, we proposed a SAR image despeckling method using a CNN platform with a new learnable spatial activation function, which required significantly fewer network parameters without incurring any degradation in performance over the state-of-the-art despeckling methods. Specifically, we redefined the rectified linear units (ReLU) function by adding a convolutional kernel to obtain the weight map of each pixel, making the activation function learnable. Meanwhile, we designed several experiments to demonstrate the advantages of our method. In total, 400 images from Google Earth comprising various scenes were selected as a training set in addition to 10 Google Earth images including athletic field, buildings, beach, and bridges as a test set, which achieved good despeckling effects in both visual and index results (peak signal to noise ratio (PSNR): 26.37 ± 2.68 and structural similarity index (SSIM): 0.83 ± 0.07 for different speckle noise levels). Extensive experiments were performed on synthetic and real SAR images to demonstrate the effectiveness of the proposed method, which proved to have a superior despeckling effect and higher ENL magnitudes than the existing methods. Our method was applied to coniferous forest, broad-leaved forest, and conifer broad-leaved mixed forest and proved to have a good despeckling effect (PSNR: 23.84 ± 1.09 and SSIM: 0.79 ± 0.02). Our method presents a robust framework inspired by the deep learning technology that realizes the speckle noise suppression for various remote sensing images. |
| first_indexed | 2024-03-10T08:05:11Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:05:11Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a6a87b7186384f639a22b5b353224eeb2023-11-22T11:08:58ZengMDPI AGRemote Sensing2072-42922021-08-011317344410.3390/rs13173444Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image AnalysisHao Wang0Zhendong Ding1Xinyi Li2Shiyu Shen3Xiaodong Ye4Dan Zhang5Shifei Tao6School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSynthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) images despeckling. However, these CNN-based SAR images despeckling approaches usually require large computational resources, especially in the case of huge training data. In this paper, we proposed a SAR image despeckling method using a CNN platform with a new learnable spatial activation function, which required significantly fewer network parameters without incurring any degradation in performance over the state-of-the-art despeckling methods. Specifically, we redefined the rectified linear units (ReLU) function by adding a convolutional kernel to obtain the weight map of each pixel, making the activation function learnable. Meanwhile, we designed several experiments to demonstrate the advantages of our method. In total, 400 images from Google Earth comprising various scenes were selected as a training set in addition to 10 Google Earth images including athletic field, buildings, beach, and bridges as a test set, which achieved good despeckling effects in both visual and index results (peak signal to noise ratio (PSNR): 26.37 ± 2.68 and structural similarity index (SSIM): 0.83 ± 0.07 for different speckle noise levels). Extensive experiments were performed on synthetic and real SAR images to demonstrate the effectiveness of the proposed method, which proved to have a superior despeckling effect and higher ENL magnitudes than the existing methods. Our method was applied to coniferous forest, broad-leaved forest, and conifer broad-leaved mixed forest and proved to have a good despeckling effect (PSNR: 23.84 ± 1.09 and SSIM: 0.79 ± 0.02). Our method presents a robust framework inspired by the deep learning technology that realizes the speckle noise suppression for various remote sensing images.https://www.mdpi.com/2072-4292/13/17/3444CNNReLUconiferbroad-leavedmixed forest |
| spellingShingle | Hao Wang Zhendong Ding Xinyi Li Shiyu Shen Xiaodong Ye Dan Zhang Shifei Tao Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis Remote Sensing CNN ReLU conifer broad-leaved mixed forest |
| title | Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis |
| title_full | Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis |
| title_fullStr | Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis |
| title_full_unstemmed | Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis |
| title_short | Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis |
| title_sort | convolutional neural network with a learnable spatial activation function for sar image despeckling and forest image analysis |
| topic | CNN ReLU conifer broad-leaved mixed forest |
| url | https://www.mdpi.com/2072-4292/13/17/3444 |
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