Research on hyper-spectral remote sensing image classification by applying stacked de-noising auto-encoders neural network

Dai, Xiaoai; He, Xu-wei; Guo, Shouheng; Liu, Senhao; Ji, Fujiang; Ruan, Huihua

doi:10.1007/s11042-021-10735-0

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…Thus came the idea of using stacked auto encoders (SAE) with some successful cases adoptions and promising prospects. In the task of hyper-spectral remote sensing, stacked autoencoders proved powerful outperforming traditional methods including principal component analysis (PCA), support vector machine (SVM) classifiers, and also combined PCA-SVM classifiers as demonstrated in [36] work. We can also see improvements in Semi-supervised learning (SSL), with only 0.08% labeled data became more reliable with closer results to supervised learning with expensive remotely sensed data [37].…”

Section: Auto-encodersmentioning

confidence: 92%

Deep learning for photovoltaic panels segmentation

Bouzaachane¹,

Darouichi²,

Guarmah³

2023

Math. Model. Comput.

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Due to advanced sensor technology, satellites and unmanned aerial vehicles (UAV) are producing a huge amount of data allowing advancement in all different kinds of earth observation applications. Thanks to this source of information, and driven by climate change concerns, renewable energy assessment became an increasing necessity among researchers and companies. Solar power, going from household rooftops to utility-scale farms, is reshaping the energy markets around the globe. However, the automatic identification of photovoltaic (PV) panels and solar farms' status is still an open question that, if answered properly, will help gauge solar power development and fulfill energy demands. Recently deep learning (DL) methods proved to be suitable to deal with remotely sensed data, hence allowing many opportunities to push further research regarding solar energy assessment. The coordination between the availability of remotely sensed data and the computer vision capabilities of deep learning has enabled researchers to provide possible solutions to the global mapping of solar farms and residential photovoltaic panels. However, the scores obtained by previous studies are questionable when it comes to dealing with the scarcity of photovoltaic systems. In this paper, we closely highlight and investigate the potential of remote sensing-driven DL approaches to cope with solar energy assessment. Given that many works have been recently released addressing such a challenge, reviewing and discussing them, it is highly motivated to keep its sustainable progress in future contributions. Then, we present a quick study highlighting how semantic segmentation models can be biased and yield significantly higher scores when inference is not sufficient. We provide a simulation of a leading semantic segmentation architecture U-Net and achieve performance scores as high as 99.78%. Nevertheless, further improvements should be made to increase the model's capability to achieve real photovoltaic units.

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Section: Auto-encodersmentioning

confidence: 92%

Deep learning for photovoltaic panels segmentation

Bouzaachane¹,

Darouichi²,

Guarmah³

2023

Math. Model. Comput.

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“…The authors in [19] propose a DL-based extracting feature process for a hyperspectral data classifier. Initially, the authors exploited an SDAE for extraction of the in-depth feature of HIS data: a huge count of unlabeled data can be pretrained for extracting the depth pixel features.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Remote Sensing Image Classification Using Improved Metaheuristic with Deep Learning

Rajalakshmi¹,

Nalini²,

Alkhayyat³

et al. 2023

Computer Systems Science and Engineering

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Remote sensing image (RSI) classifier roles a vital play in earth observation technology utilizing Remote sensing (RS) data are extremely exploited from both military and civil fields. More recently, as novel DL approaches develop, techniques for RSI classifiers with DL have attained important breakthroughs, providing a new opportunity for the research and development of RSI classifiers. This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification (ISMOGCN-HRSC) model. The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs. In the presented ISMOGCN-HRSC model, the synergic deep learning (SDL) model is exploited to produce feature vectors. The GCN model is utilized for image classification purposes to identify the proper class labels of the RSIs. The ISMO algorithm is used to enhance the classification efficiency of the GCN method, which is derived by integrating chaotic concepts into the SMO algorithm. The experimental assessment of the ISMOGCN-HRSC method is tested using a benchmark dataset.

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“…Currently, research on deep learning-based methods has increased rapidly as deep neural networks have achieved significant advances [8][9][10] . Several frameworks have been developed in combination with deep learning-based methods, including autoencoders 11 , constrained Boltzmann machines 12 , and convolutional neural networks (CNNs) 13 . Specifically, CNNs are more widely used in remote sensing image classification.…”

Section: Introductionmentioning

confidence: 99%

Joint superpixel and Transformer for high resolution remote sensing image classification

Dang,

Mao,

Zhang

et al. 2023

Preprint

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Deep neural networks combined with superpixel segmentation have proven to be superior to high-resolution remote sensingimage (HRI) classification. Currently, most HRI classification methods that combine deep learning and superpixel segmentationuse stacking on multiple scales to extract contextual information from segmented objects. However, this approach does nottake into account the contextual dependencies between each segmented object. To solve this problem, a joint superpixel andTransformer (JST) framework is proposed for HRI classification. In JST, HRI is first segmented into superpixel objects as input,and Transformer is used to model the long-range dependencies. The contextual relationship between each input superpixelobject is obtained and the class of analyzed objects is output by designing an encoding and decoding Transformer. Additionally,we explore the effect of semantic range on classification accuracy. JST is also tested by using two HRI datasets with overallclassification accuracy, average accuracy and Kappa coefficients of 0.79, 0.70, 0.78 and 0.91, 0.85, 0.89, respectively. Theeffectiveness of the proposed method is compared qualitatively and quantitatively, and the results achieve competitive andconsistently better than the benchmark comparison method.

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Research on hyper-spectral remote sensing image classification by applying stacked de-noising auto-encoders neural network

Cited by 10 publications

References 23 publications

Deep learning for photovoltaic panels segmentation

Deep learning for photovoltaic panels segmentation

Hyperspectral Remote Sensing Image Classification Using Improved Metaheuristic with Deep Learning

Joint superpixel and Transformer for high resolution remote sensing image classification

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