A Deep Learning Model for Oceanic Mesoscale Eddy Detection Based on Multi-source Remote Sensing Imagery

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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Extraction of land covers from remote sensing images based on a deep learning model of NDVI-RSU-Net

Arabian Journal of Geosciences ◽

10.1007/s12517-021-08420-5 ◽

2021 ◽

Vol 14 (20) ◽

Author(s):

Chen Yang ◽

Jingwei Hou ◽

Yanjuan Wang

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Learning Model ◽

Remote Sensing Images ◽

Land Covers ◽

Deep Learning Model

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Do Game Data Generalize Well for Remote Sensing Image Segmentation?

Remote Sensing ◽

10.3390/rs12020275 ◽

2020 ◽

Vol 12 (2) ◽

pp. 275 ◽

Cited By ~ 1

Author(s):

Zhengxia Zou ◽

Tianyang Shi ◽

Wenyuan Li ◽

Zhou Zhang ◽

Zhenwei Shi

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Video Game ◽

Real World ◽

Remote Sensing Data ◽

Ground Truth ◽

Remote Sensing Image ◽

Learning Model ◽

Image Translation ◽

Deep Learning Model

Despite the recent progress in deep learning and remote sensing image interpretation, the adaption of a deep learning model between different sources of remote sensing data still remains a challenge. This paper investigates an interesting question: do synthetic data generalize well for remote sensing image applications? To answer this question, we take the building segmentation as an example by training a deep learning model on the city map of a well-known video game “Grand Theft Auto V” and then adapting the model to real-world remote sensing images. We propose a generative adversarial training based segmentation framework to improve the adaptability of the segmentation model. Our model consists of a CycleGAN model and a ResNet based segmentation network, where the former one is a well-known image-to-image translation framework which learns a mapping of the image from the game domain to the remote sensing domain; and the latter one learns to predict pixel-wise building masks based on the transformed data. All models in our method can be trained in an end-to-end fashion. The segmentation model can be trained without using any additional ground truth reference of the real-world images. Experimental results on a public building segmentation dataset suggest the effectiveness of our adaptation method. Our method shows superiority over other state-of-the-art semantic segmentation methods, for example, Deeplab-v3 and UNet. Another advantage of our method is that by introducing semantic information to the image-to-image translation framework, the image style conversion can be further improved.

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Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification

Applied Sciences ◽

10.3390/app112411659 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11659

Author(s):

Sheng-Chieh Hung ◽

Hui-Ching Wu ◽

Ming-Hseng Tseng

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Image Quality ◽

Learning Model ◽

Image Features ◽

Quality Enhancement ◽

Scene Classification ◽

Image Quality Enhancement ◽

Learning Techniques ◽

Deep Learning Model

Through the continued development of technology, applying deep learning to remote sensing scene classification tasks is quite mature. The keys to effective deep learning model training are model architecture, training strategies, and image quality. From previous studies of the author using explainable artificial intelligence (XAI), image cases that have been incorrectly classified can be improved when the model has adequate capacity to correct the classification after manual image quality correction; however, the manual image quality correction process takes a significant amount of time. Therefore, this research integrates technologies such as noise reduction, sharpening, partial color area equalization, and color channel adjustment to evaluate a set of automated strategies for enhancing image quality. These methods can enhance details, light and shadow, color, and other image features, which are beneficial for extracting image features from the deep learning model to further improve the classification efficiency. In this study, we demonstrate that the proposed image quality enhancement strategy and deep learning techniques can effectively improve the scene classification performance of remote sensing images and outperform previous state-of-the-art approaches.

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