dPEN: deep Progressively Expanded Network for mapping heterogeneous agricultural landscape using WorldView-3 satellite imagery

Sidike, Paheding; Sagan, Vasit; Maimaitijiang, Maitiniyazi; Maimaitiyiming, Matthew; Shakoor, Nadia; Burken, Joel G.; Mockler, Todd C.; Fritschi, Felix B.

doi:10.1016/j.rse.2018.11.031

Cited by 85 publications

(48 citation statements)

References 91 publications

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“…However, there was no significant correlation found between the red-edge band and the measured AGB. This observation does not seem to be in line with earlier research findings on the importance and potential of the red-edge band in agriculture classification and forest leaf area index (LAI) estimation [28,29]. Due to the fact that the red-edge band is most likely affected by water content, the semiarid reclaimed vegetation might not be sensitive to the red-edge band [30,31].…”

Section: Relationship Of the Measured Agb With Remote Sensing Variablescontrasting

confidence: 76%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R 2 = 0.79 and RMSE = 22.82 g/m 2 ) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m −2 on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management.

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Section: Relationship Of the Measured Agb With Remote Sensing Variablescontrasting

confidence: 76%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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“…There is a good survey on DL approaches for image processing and computer vision related tasks, including image classification, segmentation, and detection [102]. For examples, single image super-resolution using CNN method [103], image denoising using block-matching CNN [104], photo aesthetic assessment using A-Lamp (Adaptive Layout-Aware Multi-Patch Deep CNN) [105], DCNN for hyperspectral imaging segmentation [106], image registration [107], fast artistic style transfer [108], image background segmentation using DCNN [109], handwritten character recognition [110], optical image classification [111], crop mapping using high-resolution satellite imagery [112], object recognition with cellular simultaneous recurrent networks and CNN [113]. The DL approaches are massively applied to human activity recognition tasks and achieved state-of-the-art performance compared to exiting approaches [114][115][116][117][118][119].…”

Section: Image Processing and Computer Visionmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Theory and Architectures

et al. 2019

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In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

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“…It is difficult to optimize a deep network architecture, especially under the context of PolSAR image classification, which is a task with poor data diversity. Regularization methods are common techniques to alleviate this problem, such as ReLU activation, batch normalization and skip connections (Sidike et al, 2019). Additional classifiers bring the additional task of enhancing the presentation ability of the side outputs, which can also be seen as a variant of regularization.…”

Section: Discussionmentioning

confidence: 99%

Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework

Zhang

Dong

Zou

2019

ISPRS Journal of Photogrammetry and Remote Sensing

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Convolutional neural networks (CNNs) have been widely used to improve the accuracy of polarimetric synthetic aperture radar (PolSAR) image classification. However, in most studies, the difference between PolSAR images and optical images is rarely considered. Most of the existing CNNs are not tailored for the task of PolSAR image classification, in which complex-valued PolSAR data have been simply equated to real-valued data to fit the optical image processing architectures and avoid complex-valued operations. This is one of the reasons CNNs unable to perform their full capabilities in PolSAR classification. To solve the above problem, the objective of this paper is to develop a tailored CNN framework for PolSAR image classification, which can be implemented from two aspects: Seeking a better form of PolSAR data as the input of CNNs and building matched CNN architectures based on the proposed input form. In this paper, considering the properties of complexvalued numbers, amplitude and phase of complex-valued PolSAR data are extracted as the input for the first time to maintain the integrity of original information while avoiding immature complex-valued operations. Then, a multi-task CNN (MCNN) architecture is proposed to match the improved input form and achieve better classification results. Furthermore, depthwise separable convolution is introduced to the proposed architecture in order to better extract information from the phase information. Experiments on three PolSAR benchmark datasets not only prove that using amplitude and phase as the input do contribute to the improvement of PolSAR classifica-

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dPEN: deep Progressively Expanded Network for mapping heterogeneous agricultural landscape using WorldView-3 satellite imagery

Cited by 85 publications

References 91 publications

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework

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