scholarly journals Grand Challenges in Remote Sensing Image Analysis and Classification

2021 ◽  
Vol 1 ◽  
Author(s):  
Christopher Small
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Image ◽  
Grand Challenges

Weighted model-based clustering for remote sensing image analysis

2009 ◽  
Vol 14 (1) ◽  
pp. 125-136 ◽  
Author(s):  
Joseph W. Richards ◽  
Johanna Hardin ◽  
Eric B. Grosfils
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Image ◽  
Model Based Clustering ◽  
Model Based ◽  
Weighted Model

scholarly journals Introduction to the Special Issue “Uncertainty in Remote Sensing Image Analysis”

2018 ◽  
Vol 10 (12) ◽  
pp. 1975
Author(s):  
Alfred Stein ◽  
Yong Ge ◽  
Inger Fabris-Rotelli
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Image ◽  
Special Issue

Images obtained from satellites are of an increasing resolution. [...]

Airborne remote sensing in forestry: Sensors, analysis and applications

2000 ◽  
Vol 76 (6) ◽  
pp. 859-876 ◽  
Author(s):  
Douglas J. King
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Key Words ◽  
Analytical Techniques ◽  
Remote Sensing Image ◽  
Digital Cameras ◽  
Airborne Remote Sensing ◽  
Hyperspectral Sensors ◽  
Imaging Characteristics ◽  
Laser Remote Sensing

This paper discusses the aspects of airborne remote sensing that are critical to forestry applications, the imaging characteristics of the most common sensors currently available, and analytical techniques that make use of the great amount of information content in airborne imagery. As the first paper in the CIF technical meeting to which this issue of the Forestry Chronicle is devoted, the paper is intended to provide an overview and context for subsequent papers and not a presentation of specific research methods or results. Key words: airborne remote sensing, forestry, photography, digital cameras, hyperspectral sensors, radar, laser remote sensing, image analysis

scholarly journals A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images

2021 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Melike Ilteralp ◽  
Sema Ariman ◽  
Erchan Aptoula
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Image Analysis ◽  
Chlorophyll A ◽  
Water Quality Assessment ◽  
Remote Sensing Image ◽  
Semisupervised Learning ◽  
Main Task ◽  
Chl A ◽  
Multitemporal Remote Sensing

This article addresses the scarcity of labeled data in multitemporal remote sensing image analysis, and especially in the context of Chlorophyll-a (Chl-a) estimation for inland water quality assessment. We propose a multitask CNN architecture that can exploit unlabeled satellite imagery and that can be generalized to other multitemporal remote sensing image analysis contexts where the target parameter exhibits seasonal fluctuations. Specifically, Chl-a estimation is set as the main task, and an unlabeled sample’s month classification is set as an auxiliary network task. The proposed approach is validated with multitemporal/spectral Sentinel-2 images of Lake Balik in Turkey using in situ measurements acquired during 2017–2019. We show that harnessing unlabeled data through multitask learning improves water quality estimation performance.

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