scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1246
Author(s):  
Kyle B. Larson ◽  
Aaron R. Tuor
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Biological Invasions ◽  
Spatial Information ◽  
Wildlife Habitat ◽  
Ground Sample ◽  
Sensing Applications ◽  
Combining Data ◽  
Remote Sensing Applications

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

scholarly journals Approaches for Hyperspectral Image Classification Detailed Review

2021 ◽  
Vol 11 (1) ◽  
pp. 13-22
Author(s):  
Kushalatha M R ◽  
◽  
Prasantha H S ◽  
Beena R. Shetty ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Support Vector ◽  
Electromagnetic Spectrum ◽  
Hyperspectral Image Classification ◽  
Sensing Applications ◽  
Remote Sensing Applications

Hyperspectral Image (HSI) processing is the new advancement in image / signal processing field. The growth over the years is appreciable. The main reason behind the successful growth of the Hyperspectral imaging field is due to the enormous amount of spectral and spatial information that the imagery contains. The spectral band that the HSI which contains is also more in number. When an image is captured through the HSI cameras, it contains around 200-250 images of the same scene. Nowadays HSI is used extensively in the fields of environmental monitoring, Crop-Field monitoring, Classification, Identification, Remote sensing applications, Surveillance etc. The spectral and spatial information content present in Hyperspectral images are with high resolutions.Hyperspectral imaging has shown significant growth and widely used in most of the remote sensing applications due to its presence of information of a scene over hundreds of contiguous bands In. Hyperspectral Image Classification of materials is the critical application of HSI using Hyperspectral sensors. It collects hundreds of spectrum channels, where each channel consists of a sharp point of Electromagnetic Spectrum. The paper mainly focuses on Deep Learning techniques such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Support Vector machines (SVM), K-Nearest Neighbour (KNN) for the accuracy in classification. Finally in the summary the current state-of-the-art scheme, a critical discussion after reviewing the research work by other professionals and organizing it into review-based paper, also implying about the present status on classification accuracy using neural networks is carried out.

RNN-based multispectral satellite image processing for remote sensing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkata Dasu Marri ◽  
Veera Narayana Reddy P. ◽  
Chandra Mohan Reddy S.
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Image Classification ◽  
Satellite Image ◽  
Multispectral Image ◽  
Content Type ◽  
Colliding Bodies Optimization ◽  
Sensing Applications ◽  
Remote Sensing Applications

Purpose Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy. Design/methodology/approach This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image. Findings The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods. Originality/value In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

scholarly journals Deep learning in remote sensing applications: A meta-analysis and review

2019 ◽  
Vol 152 ◽  
pp. 166-177 ◽  
Author(s):  
Lei Ma ◽  
Yu Liu ◽  
Xueliang Zhang ◽  
Yuanxin Ye ◽  
Gaofei Yin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Meta Analysis ◽  
Sensing Applications ◽  
Remote Sensing Applications

scholarly journals Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4453 ◽  
Author(s):  
Mamaghani ◽  
Salvaggio
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Laboratory Method ◽  
Sensor Calibration ◽  
Radiometric Calibration ◽  
End User ◽  
Sensing Applications ◽  
A Value ◽  
Remote Sensing Applications

This paper focuses on the calibration of multispectral sensors typically used for remote sensing. These systems are often provided with "factory" radiometric calibration and vignette correction parameters. These parameters, which are assumed to be accurate when the sensor is new, may change as the camera is utilized in real-world conditions. As a result, regular calibration and characterization of any sensor should be conducted. An end-user laboratory method for computing both the vignette correction and radiometric calibration function is discussed in this paper. As an exemplar, this method for radiance computation is compared to the method provided by MicaSense for their RedEdge series of sensors. The proposed method and the method provided by MicaSense for radiance computation are applied to a variety of images captured in the laboratory using a traceable source. In addition, a complete error propagation is conducted to quantify the error produced when images are converted from digital counts to radiance. The proposed methodology was shown to produce lower errors in radiance imagery. The average percent error in radiance was −10.98%, −0.43%, 3.59%, 32.81% and −17.08% using the MicaSense provided method and their "factory" parameters, while the proposed method produced errors of 3.44%, 2.93%, 2.93%, 3.70% and 0.72% for the blue, green, red, near infrared and red edge bands, respectively. To further quantify the error in terms commonly used in remote sensing applications, the error in radiance was propagated to a reflectance error and additionally used to compute errors in two widely used parameters for assessing vegetation health, NDVI and NDRE. For the NDVI example, the ground reference was computed to be 0.899 ± 0.006, while the provided MicaSense method produced a value of 0.876 ± 0.005 and the proposed method produced a value of 0.897 ± 0.007. For NDRE, the ground reference was 0.455 ± 0.028, MicaSense method produced 0.239 ± 0.026 and the proposed method produced 0.435 ± 0.038.

THE APPLICATION OF REMOTE SENSING IN PRECISION AGRICULTURE

1970 ◽  
pp. 10-16
Author(s):  
S. Yu. Blokhina
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Crop Yields ◽  
Temporal Frequency ◽  
Organic Matter Content ◽  
Sensing Applications ◽  
Remote Sensing Applications

The paper provides an overview of foreign literature on the remote sensing applications in precision agriculture. Remote sensing applications in precision agriculture began with sensors for soil organic matter content, and have quickly advanced to include hand held sensors to tractor or aerial or satellite mounted sensors. Wavelengths of electromagnetic radiation initially focused on a few key visible or near infrared bands, and nowadays electromagnetic wavelengths in use range from the ultraviolet to microwave portions of the spectrum. Spectral bandwidth has decreased dramatically with the advent of hyperspectral remote sensing, allowing improved analysis of crop stress, crop biophysical or biochemical characteristics and specific compounds. A variety of spectral indices have been widely implemented within various precision agriculture applications, rather than a focus on only normalized difference vegetation indices. Spatial resolution and temporal frequency of remote sensing imagery has increased significantly, allowing evaluation of soil and crop properties at fine spatial resolution at the expense of increased data storage and processing requirements. At present there is considerable interest in collecting remote sensing for operational management of soil and crop yields, as well as control over the spread of pests and weeds practically in real time.

X-ray diffraction and infrared characterization of Oxisols from central and southeastern Brazil

Clay Minerals ◽  
2008 ◽  
Vol 43 (4) ◽  
pp. 549-560 ◽  
Author(s):  
R. P. Nitzsche ◽  
J. B. Percival ◽  
J. K. Torrance ◽  
J. A. R. Stirling ◽  
J. T. Bowen
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Microwave Remote Sensing ◽  
Short Wave ◽  
Southeastern Brazil ◽  
X Ray Diffraction ◽  
X Ray ◽  
Sensing Applications ◽  
Remote Sensing Applications

AbstractEleven Oxisols with high clay contents, 2.6–59.7 wt.% Fe2O3, and containing hematite, goethite, magnetite and maghemite, from São Paulo, Minas Gerais and Goiás, Brazil, were studied for the purpose of microwave remote sensing applications in the 0.3 to 300 GHz range. Of special interest are: the pseudosand effect caused by Fe-oxide cementation of clusters of soil particles; the mineralogy; and whether the soil magnetic susceptibility affected by ferromagnetic magnetite and maghemite interferes with microwave propagation. Quantitative mineralogical analyses were conducted using X-ray diffraction with Rietveld refinement. Visible, near infrared and short wave infrared spectroscopic analyses were used to characterize the samples qualitatively for comparison with published spectral radiometry results. Quartz (3–88%), hematite (2–36%) and gibbsite (1–40%) occurred in all soils, whereas kaolinite (2–70%) and anatase (2–13%) occurred in nine samples. Ilmenite (1–8%) was found in eight soils and goethite (2–39%) in seven. Of the ferromagnetic minerals, maghemite occurred in seven soils (1–13%) and three contained magnetite (<2%). These results will be applied to the interpretation of the effect of Fe oxides, particularly the ferromagnetic oxides, on microwave interaction with high-Fe soils, with ultimate application to the monitoring of soil water content by microwave remote sensing.

scholarly journals High-resolution, non-scanning FTS for remote sensing

1995 ◽  
Vol 149 ◽  
pp. 338-339
Author(s):  
K. Døhlen ◽  
A. Cañas
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
High Resolution ◽  
Near Infrared ◽  
Michelson Interferometer ◽  
Resolving Power ◽  
Diode Array ◽  
Sensing Applications ◽  
First Results ◽  
Remote Sensing Applications

We present the first results from a portable spectrometer for the visible and very near infrared based upon the principle of heterodyned holographic Fourier transform spectroscopy (HHS) (Dohi and Suzuki 1971, Dohlen 1994). The instrument uses a Michelson interferometer where one of the mirrors is replaced with a grating. This produces a spatially located, frequency-shifted interferogram which is read out by an all-reflective relay lens and a photo-diode array and processed on a portable PC. A battery pack ensures an autonomy of about 7 hours. Instrumental assets include high optical throughput, variable resolving power, and no moving parts.We have successfully used the instrument in two different remote sensing applications: detection of vegetation reflectance and atmospheric absorption.

scholarly journals Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sensing Imagery

2021 ◽  
Vol 13 (8) ◽  
pp. 1598
Author(s):  
Bidroha Basu ◽  
Srikanta Sannigrahi ◽  
Arunima Sarkar Basu ◽  
Francesco Pilla
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Marine Ecosystem ◽  
Training Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Real World Data ◽  
Plastic Debris ◽  
Fuzzy C Means ◽  
Sentinel 2

Plastic pollution poses a significant environmental threat to the existence and health of biodiversity and the marine ecosystem. The intrusion of plastic to the food chain is a massive concern for human health. Urbanisation, population growth, and tourism have been identified as major contributors to the growing rate of plastic debris, particularly in waterbodies such as rivers, lakes, seas, and oceans. Over the past decade, many studies have focused on identifying the waterbodies near the coastal regions where a high level of accumulated plastics have been found. This research focused on using high-resolution Sentinel-2 satellite remote sensing images to detect floating plastic debris in coastal waterbodies. Accurate detection of plastic debris can help in deploying appropriate measures to reduce plastics in oceans. Two unsupervised (K-means and fuzzy c-means (FCM)) and two supervised (support vector regression (SVR) and semi-supervised fuzzy c-means (SFCM)) classification algorithms were developed to identify floating plastics. The unsupervised classification algorithms consider the remote sensing data as the sole input to develop the models, while the supervised classifications require in situ information on the presence/absence of floating plastics in selected Sentinel-2 grids for modelling. Data from Cyprus and Greece were considered to calibrate the supervised models and to estimate model efficiency. Out of available multiple bands of Sentinel-2 data, a combination of 6 bands of reflectance data (blue, green, red, red edge 2, near infrared, and short wave infrared 1) and two indices (NDVI and FDI) were selected to develop the models, as they were found to be most efficient for detecting floating plastics. The SVR-based supervised classification has an accuracy in the range of 96.9–98.4%, while that for SFCM and FCM clustering are between 35.7 and 64.3% and 69.8 and 82.2%, respectively, and for K-means, the range varies from 69.8 to 81.4%. It needs to be noted that the total number of grids with floating plastics in real-world data considered in this study is 59, which needs to be increased considerably to improve model performance. Training data from other parts of the world needs to be collected to investigate the performance of the classification algorithms at a global scale.

Sign in / Sign up

Export Citation Format

Share Document