Automatic land cover classification of multi-resolution dualpol data using convolutional neural network (CNN)

Memon, Nimrabanu; Parikh, Hemani; Patel, Samir; Patel, Deep; Patel, Vibha

doi:10.1016/j.rsase.2021.100491

Cited by 14 publications

(13 citation statements)

References 26 publications

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“…Recently, the land use/land cover change has been classified using deep learning algorithms (convolutional neural networks, CNN). As improved by several researchers [17][18][19][20][21][22]50] the result is also impressive for input parameters for groundwater potentiality prediction. As reported by Calderon-Loor et al [51] land cover can be classified as follows: cropland (rainfed and irrigated cropping area (permanent and annual)), forest (includes open, closed, scattered, and sparse trees), grassland (rainfed and irrigated managed and native pastures, tussock, chenopods, and hummock grasses), built-up (human-made surfaces areas inside urban centers and buffer zones), water (permanent water bodies), and other areas (includes mines, wetlands, bare lands, and salt lakes).…”

Section: D Convolutional Neuralmentioning

confidence: 75%

“…Recently, many researchers have used the CNN approach for the evaluation of groundwater influencing factors [16] such as land use/land cover change classification [17][18][19][20][21][22] and prediction groundwater potentiality [16,23]. Typically, frameworks of CNN are two principal approaches, i.e., patch-based and end-to-end (pixel-to-pixel), for the classification of semantic pixel-based techniques.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, CNN has a great role for soil classification [25][26][27], hydrogeological classification [19], lithology [28], soil permeability [29], land temperature forecasting [30], flood susceptibility map [10,21], predicting groundwater level and flow [31][32][33], and prediction of rainfall [34]. CNN can be applied in the recognition of waste type [11] and groundwater quality prediction.…”

Section: Introductionmentioning

confidence: 99%

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Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

Tegegne

2022

Journal of Engineering

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In the field of groundwater engineering, a convolutional neural network (CNN) has become a great role to assess the spatial groundwater potentiality zones and land use/land cover changes based on remote sensing (RS) technology. CNN can be offering a great potential to extract complex spatial features with multiple high levels of generalization. However, geometric distortion and fuzzy entity boundaries as well as a huge data preparation severance may be the main constraint and affect the spatial potential of CNN application for land cover classification. This study aims to recognize the proficiency of deep learning algorithms, i.e., CNN, for spatial assessment of groundwater potential zones and land cover. Among the groundwater influencing factors, classification of land cover (agriculture, built-up, water bodies, forests, and bare land) has been reported by several researchers for different purposes and they approved the CNN capability for the prediction of spatial groundwater potentiality zones like very high, high, moderate, poor, and very poor areas. In this study, CNN is recommended as a very essential algorithm for the identification of groundwater potential zones and classification of land use/land cover change. CNN gives a better option for scholars regarding when the limited data sets are available for validation.

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Section: D Convolutional Neuralmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

Tegegne

2022

Journal of Engineering

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“…Since the CNN algorithms need a lot of training data to perform [23], transfer learning has been used in a number of classification applications [24][25][26][27]. The learning methodologies have determined four kinds of transfer learning [28]: (1) instance-based transfer learning, (2) feature-based transfer learning, (3) model-based transfer learning, and (4) relation-based transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Sun

et al. 2022

Remote Sensing

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Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37%~95.25%.

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“…Complex valued 3D-CNN called as CV-3D-CNN has shown superior classification results compared to previous CNN based methods. Efficiency of CNN is tested on multi-resolution dual-Pol images for land cover classification in 28 . A new structure of CNN is proposed for PolSAR image classification in 29 .…”

Section: Introductionmentioning

confidence: 99%

Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence

Imani

2022

Sci Rep

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Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but they need a high volume of labeled samples, which are not usually available in practice, or they cause a high computational burden for implementation. In this work, instead of spending cost for network training, the inherent nature of PolSAR image is used for generation of convolutional kernels for extraction of deep and robust features. Moreover, extraction of diverse scattering characteristics contained in the coherency matrix of PolSAR and fusion of their output classification results with a high confidence have high impact in providing a reliable classification map. The introduced method called discriminative features based high confidence classification (DFC) utilizes several approaches to deal with difficulties of PolSAR image classification. It uses a multi-view analysis to generate diverse classification maps with different information. It extracts deep polarimetric-spatial features, consistent and robust with respect to the original PolSAR data, by applying several pre-determined convolutional filters selected from the important regions of image. Convolutional kernels are fixed without requirement to be learned. The important regions are determined with selecting the key points of image. In addition, a two-step discriminant analysis method is proposed to reduce dimensionality and result in a feature space with minimum overlapping and maximum class separability. Eventually, a high confidence decision fusion is implemented to find the final classification map. Impact of multi-view analysis, selection of important regions as fixed convolutional kernels, two-step discriminant analysis and high confidence decision fusion are individually assessed on three real PolSAR images in different sizes of training sets. For example, the proposed method achieves 96.40% and 98.72% overall classification accuracy by using 10 and 100 training samples per class, respectively in L-band Flevoland image acquired by AIRSAR. Generally, the experiments show high efficiency of DFC compared to several state-of-the-art methods especially for small sample size situations.

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Automatic land cover classification of multi-resolution dualpol data using convolutional neural network (CNN)

Cited by 14 publications

References 26 publications

Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence

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