scholarly journals Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks

2018 ◽  
Vol 7 (3) ◽  
pp. 110 ◽  
Author(s):  
Rui Guo ◽  
Jianbo Liu ◽  
Na Li ◽  
Shibin Liu ◽  
Fu Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
High Resolution ◽  
Deep Neural Networks ◽  
Classification Method ◽  
Remote Sensing Imagery

scholarly journals An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology

2017 ◽  
Vol 9 (4) ◽  
pp. 329 ◽  
Author(s):  
Haiyan Gu ◽  
Haitao Li ◽  
Li Yan ◽  
Zhengjun Liu ◽  
Thomas Blaschke ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Classification Method ◽  
Semantic Classification ◽  
Remote Sensing Imagery ◽  
Object Based

Classification of high resolution hyperspectral remote sensing data using deep neural networks

2018 ◽  
Vol 34 (4) ◽  
pp. 2273-2285 ◽  
Author(s):  
Mehmet Emin Yuksel ◽  
Nurcan Sarikaya Basturk ◽  
Hasan Badem ◽  
Abdullah Caliskan ◽  
Alper Basturk
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
High Resolution ◽  
Deep Neural Networks ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data

scholarly journals Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks

2019 ◽  
Vol 11 (1) ◽  
pp. 69 ◽  
Author(s):  
Zachary L. Langford ◽  
Jitendra Kumar ◽  
Forrest M. Hoffman ◽  
Amy L. Breen ◽  
Colleen M. Iversen
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Ecosystem Structure ◽  
Modeling And Analysis ◽  
High Resolution Imagery

Land cover datasets are essential for modeling and analysis of Arctic ecosystem structure and function and for understanding land–atmosphere interactions at high spatial resolutions. However, most Arctic land cover products are generated at a coarse resolution, often limited due to cloud cover, polar darkness, and poor availability of high-resolution imagery. A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska. The fusion of hyperspectral, multispectral, and terrain datasets was performed using unsupervised and supervised classification techniques over a ∼343 km2 area, and a high-resolution (5 m) vegetation classification map was generated. An unsupervised technique was developed to classify high-dimensional remote sensing datasets into cohesive clusters. We employed a quantitative method to add supervision to the unlabeled clusters, producing a fully labeled vegetation map. We then developed convolutional neural networks (CNNs) using the multi-sensor fusion datasets to map vegetation distributions using the original classes and the classes produced by the unsupervised classification method. To validate the resulting CNN maps, vegetation observations were collected at 30 field plots during the summer of 2016, and the resulting vegetation products developed were evaluated against them for accuracy. Our analysis indicates the CNN models based on the labels produced by the unsupervised classification method provided the most accurate mapping of vegetation types, increasing the validation score (i.e., precision) from 0.53 to 0.83 when evaluated against field vegetation observations.

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