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 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>

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 Geo Information Systems &Remote Sensing: Applications in Environmental Systems & Management

2020 ◽  
pp. 11-18
Author(s):  
P.K. Paul ◽  
P. S. Aithal ◽  
A. Bhuimali ◽  
K.S. Tiwary ◽  
R. Saavedra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Information Science ◽  
Spatial Information ◽  
Physical Content ◽  
Gis And Remote Sensing ◽  
Interdisciplinary Field ◽  
Sensing Applications ◽  
Database Technology ◽  
Remote Sensing Applications

Geo Informatics is an interdisciplinary field responsible for spatial information related activities. Geo Informatics is close to the Geo Information Science, Geo Information System, Remote Sensing, etc. Geo Informatics is a combination of Geo Science and Information Science and here different kinds of IT and Computing tools are being used such as Database Technology, Network Technology, Web Technology, Multimedia Technology, etc in the spatial data management. Remote Sensing is considered as a component of Geo Information Science dedicated in gathering of information on the different types of objects without physical content and applicable in different areas of the geography, survey of land and different type of geo related areas viz. Hydrology, Ecology, Meteorology, Oceanography and Geology, etc. The term remote sensing is also called as GIS & RS due to their relationship and their importance. The applications of the IT in Geography and allied areas are called as Geo Informatics or Geo Information Science. Similarly, the applications and utilization of IT, Information Science and Computing in Environment and allied areas are known as Environmental Informatics or Environmental Information Science. The GIS and Remote Sensing applications in the environment and ecological areas are increasing rapidly and it includes various existing and emerging applications. This paper talks about the applications of the GIS and RS in Environmental Applications and Management.

scholarly journals DOMAIN ADAPTATION WITH CYCLEGAN FOR CHANGE DETECTION IN THE AMAZON FOREST

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1635-1643
Author(s):  
P. J. Soto ◽  
G. A. O. P. Costa ◽  
R. Q. Feitosa ◽  
P. N. Happ ◽  
M. X. Ortega ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Change Detection ◽  
Domain Adaptation ◽  
Fine Tuning ◽  
Classification Model ◽  
Generative Adversarial Networks ◽  
Amazon Forest ◽  
Sensing Applications ◽  
Remote Sensing Applications

Abstract. Deep learning classification models require large amounts of labeled training data to perform properly, but the production of reference data for most Earth observation applications is a labor intensive, costly process. In that sense, transfer learning is an option to mitigate the demand for labeled data. In many remote sensing applications, however, the accuracy of a deep learning-based classification model trained with a specific dataset drops significantly when it is tested on a different dataset, even after fine-tuning. In general, this behavior can be credited to the domain shift phenomenon. In remote sensing applications, domain shift can be associated with changes in the environmental conditions during the acquisition of new data, variations of objects’ appearances, geographical variability and different sensor properties, among other aspects. In recent years, deep learning-based domain adaptation techniques have been used to alleviate the domain shift problem. Recent improvements in domain adaptation technology rely on techniques based on Generative Adversarial Networks (GANs), such as the Cycle-Consistent Generative Adversarial Network (CycleGAN), which adapts images across different domains by learning nonlinear mapping functions between the domains. In this work, we exploit the CycleGAN approach for domain adaptation in a particular change detection application, namely, deforestation detection in the Amazon forest. Experimental results indicate that the proposed approach is capable of alleviating the effects associated with domain shift in the context of the target application.

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