scholarly journals Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network

2020 ◽  
Author(s):  
Jian Sun ◽  
Fangcao Xu ◽  
Guido Cervone ◽  
Melissa Gervais ◽  
Christelle Wauthier ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Neural Network ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Time Dependent ◽  
Sensing Data

scholarly journals Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network

2021 ◽  
Vol 174 ◽  
pp. 117-131
Author(s):  
Jian Sun ◽  
Fangcao Xu ◽  
Guido Cervone ◽  
Melissa Gervais ◽  
Christelle Wauthier ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Neural Network ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Time Dependent ◽  
Sensing Data

scholarly journals Agricultural Stress Monitoring using Remote Sensing Data

2020 ◽  
Vol 9 (3) ◽  
pp. 335-337
Keyword(s):  
Remote Sensing ◽  
Stress Level ◽  
Hyperspectral Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Stress Monitoring ◽  
Chlorophyll Contents ◽  
Sensing Data

This study consist of experiments on Hyperspectral remote sensing data for monitoring field stress using remote sensing tools. We have segmented Hyperspectral image and then calculated stress level using ENVI tool. EO-I hyperspectral remote sensing data from hyperion space born sensor has been used as the key input. QUACK (Quick Atmospheric Correction) algorithm has been used for atmospheric correction of hyperspectral data. EO-1, hyperion sensors data It has been observed that stress level depends on chlorophyll contents of a leaf. It has been observed that green field is with less stress and rock where no chlorophyll contents have most stress. We have also shown stress level in the scale of 1 to 9.

Classification method of CO2 hyperspectral remote sensing data based on neural network

2020 ◽  
Vol 156 ◽  
pp. 124-130
Author(s):  
Le Zhang ◽  
Jinsong Wang ◽  
Zhiyong An
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Classification Method ◽  
Sensing Data

scholarly journals Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0254542
Author(s):  
Zhengyang Wang ◽  
Shufang Tian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Information Extraction ◽  
Remote Sensing Data ◽  
Kappa Coefficient ◽  
Hyperspectral Remote Sensing ◽  
Classification Model ◽  
Backpropagation Neural Network ◽  
Sensing Data

The purposes are to solve the isomorphism encountered while processing hyperspectral remote sensing data and improve the accuracy of hyperspectral remote sensing data in extracting and classifying lithological information. Taking rocks as the research object, Backpropagation Neural Network (BPNN) is introduced. After the hyperspectral image data are normalized, the lithological spectrum and spatial information are the feature extraction targets to construct a deep learning-based lithological information extraction model. The performance of the model is analyzed using specific instance data. Results demonstrate that the overall accuracy and the Kappa coefficient of the lithological information extraction and classification model based on deep learning were 90.58% and 0.8676, respectively. This model can precisely distinguish the properties of rock masses and provide better performance compared with the state of other analysis models. After introducing deep learning, the recognition accuracy and the Kappa coefficient of the proposed BPNN model increased by 8.5% and 0.12, respectively, compared with the traditional BPNN. The proposed extraction and classification model can provide some research values and practical significances for the hyperspectral rock and mineral classification.

New algorithm for atmospheric correction of hyperspectral remote sensing data

2001 ◽  
Author(s):  
Marcos J. Montes ◽  
Bo-Cai Gao ◽  
Curtiss O. Davis
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data

Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean

2009 ◽  
Vol 113 ◽  
pp. S17-S24 ◽  
Author(s):  
Bo-Cai Gao ◽  
Marcos J. Montes ◽  
Curtiss O. Davis ◽  
Alexander F.H. Goetz
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data

scholarly journals Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing

2020 ◽  
Vol 12 (17) ◽  
pp. 2678
Author(s):  
Xiaoling Deng ◽  
Zihao Zhu ◽  
Jiacheng Yang ◽  
Zheng Zheng ◽  
Zixiao Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Feature Fusion ◽  
Spectral Feature ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Detection Methods ◽  
Sensing Data

Citrus is an important cash crop in the world, and huanglongbing (HLB) is a destructive disease in the citrus industry. To efficiently detect the degree of HLB stress on large-scale orchard citrus trees, an UAV (Uncrewed Aerial Vehicle) hyperspectral remote sensing tool is used for HLB rapid detection. A Cubert S185 (Airborne Hyperspectral camera) was mounted on the UAV of DJI Matrice 600 Pro to capture the hyperspectral remote sensing images; and a ASD Handheld2 (spectrometer) was used to verify the effectiveness of the remote sensing data. Correlation-proven UAV hyperspectral remote sensing data were used, and canopy spectral samples based on single pixels were extracted for processing and analysis. The feature bands extracted by the genetic algorithm (GA) of the improved selection operator were 468 nm, 504 nm, 512 nm, 516 nm, 528 nm, 536 nm, 632 nm, 680 nm, 688 nm, and 852 nm for the HLB detection. The proposed HLB detection methods (based on the multi-feature fusion of vegetation index) and canopy spectral feature parameters constructed (based on the feature band in stacked autoencoder (SAE) neural network) have a classification accuracy of 99.33% and a loss of 0.0783 for the training set, and a classification accuracy of 99.72% and a loss of 0.0585 for the validation set. This performance is higher than that based on the full-band AutoEncoder neural network. The field-testing results show that the model could effectively detect the HLB plants and output the distribution of the disease in the canopy, thus judging the plant disease level in a large area efficiently.

scholarly journals KLASIFIKASI PENUTUP/PENGGUNAAN LAHAN DENGAN DATA SATELIT PENGINDERAAN JAUH HIPERSPEKTRAL (HYPERION) MENGGUNAKAN METODE NEURAL NETWORK TIRUAN

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Dony Kushardono
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Near Infrared ◽  
Back Propagation ◽  
Remote Sensing Data ◽  
Back Propagation Neural Network ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data ◽  
Test Input ◽  
Long Time

Hyperspectral remote sensing data has numerous spectral information for the land-use/land-cover (LULC) classification, but a large number of hyperspectral band data is becoming a problem in the LULC classification. This research proposes the use of the back propagation neural network for LULC classification with hyperspectral remote sensing data. Neural network used in this study is three layers, in which to test input layer has a number of neurons as many as 242 to process all band data, 163 neurons, and 50 neurons to process the data band has a high average digital number, and data bands at wavelengths of visible to near infrared. The results showed the use of all the data band hyperspectral on classification with the neural network has the highest classification accuracy of up to 98% for 18 LULC class, but it takes a very long time. Selecting a number of bands of precise data for classification with a neural network, in addition to speeding up data processing time, can also provide sufficient accuracy classification results.ABSTRAKData penginderaan jauh hiperspektral memiliki informasi spektral yang sangat banyak untuk klasifikasi penutup/penggunaan lahan (LULC), akan tetapi banyaknya jumlah band data hiperspektral menjadi masalah dalam klasifikasi LULC. Penelitian ini mengusulkan penggunaan back propagation neural network untuk klasifikasi LULC dengan data penginderaan jauh hiperspektral. Neural network yang dipergunakan 3 lapis, dimana untuk uji coba lapis masukan memiliki jumlah neuron sebanyak 242 untuk mengolah seluruh band, 163 neuron, dan 50 neuron untuk mengolah data band yang memiliki nilai digital rataan yang tinggi, dan data band pada panjang gelombang cahaya tampak hingga infra merah dekat. Hasil penelitian menunjukkan penggunaan seluruh band data hiperspektral pada klasifikasi dengan neural network memiliki akurasi hasil klasifikasi tertinggi hingga 98% untuk 18 kelas LULC, akan tetapi waktu yang diperlukan sangat lama. Pemilihan sejumlah band data yang tepat untuk klasifikasi dengan neural network, selain mempercepat waktu pengolahan data, juga bisa memberikan akurasi hasil klasifikasi yang mencukupi.

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