Remote Sensing And Image Interpretation

By U. Beyer. 2016

3,059 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b2364d1ba5eb90c562a2b98102c423c230abf801

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Citations in the corpus

A Deep Journey into Super-resolution (2019, background): “, object detection in scenes [23] (particularly small objects [4]), face recognition in surveillance videos [64], medical imaging [25], improving interpretation of images in remote sensing [53], astronomical images [55], and forensics [80].”
Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives (2014, background): “Remote sensing (RS) refers to the science aimed at collecting Earth Observations (EO) by using non-contact techniques [5].”
The Indian Sundarban Mangrove Forests: History, Utilization, Conservation Strategies and Local Perception (2015, background): “Conventional satellite data such as Landsat allowed monitoring land cover changes from the 1970s [38].”
Modern Trends in Hyperspectral Image Analysis: A Review (2018, background): “Such plots are labeled as spectral signatures or spectral response curves’’ [21].”
Urban Land Use Land Cover Changes and Their Effect on Land Surface Temperature: Case Study Using Dohuk City in the Kurdistan Region of Iraq (2017, background): “However, Lillesand et al, 2008 [43] noted the need for 20 training samples of 40 pixels for each land cover category.”
A compilation of UAV applications for precision agriculture (2020, unknown): “RS technologies refer to obtaining information remotely, by using ICT services [50–54] .”
Illumination Invariant Imaging : Applications in Robust Vision-based Localisation , Mapping and Classification for Autonomous Vehicles (2014, background): “The process of inferring physical properties of objects from imagery is often referred to as passive remote sensing, and is a common in the field of satellite observation [6].”
CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS (2018, background): “Unfortunately, the errors from classification are propagated into the final change map (Lillesand et al., 2008).”
A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques (2016, background): “Spectral band ratios can reduce irradiance, atmospheric and air-water surface influences in the remotely sensed signal [86,96].”
Deep learning for change detection in remote sensing: a review (2022, background): “However, errors stemming from the classification maps are compounded in the final change map, thus reducing the accuracy of the final change detection result (Chan, Chan, and Yeh 2001; Dai, and Khorram 1999; Lillesand, Kiefer, and Chipman 2015).”
A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, methodology): “…an image analyst selects representative sample sites with known class types (i.e. training samples), and compares the spectral properties of each pixel in the image with those of the training samples, then labels the pixel to the class type according to decision rules [Lillesand et al., 2004].”
Evaluation of TanDEM-X DEMs on selected Brazilian sites: Comparison with SRTM, ASTER GDEM and ALOS AW3D30 (2017, background): “the pixel of interest; the window moves one pixel at a time across the raster map, until the whole area is processed (Demers, 2004; Lillesand et al., 2004; Grohmann and Riccomini, 2009).”
Sparse Recovery of Hyperspectral Signal from Natural RGB Images (2016, background): “…Systems (HISs) has been limited to those domains and applications in which these aspects of the signal (either spatial, but mostly temporal resolution) were not central – remote sensing (cf. [2]), agriculture (cf. [3]), geology (cf. [4]), astronomy (cf. [5]), earth sciences (cf. [6]), and others.”
Monitoring and Modeling the Patterns and Trends of Urban Growth Using Urban Sprawl Matrix and CA-Markov Model: A Case Study of Karachi, Pakistan (2021, methodology): “As an inherent part for geospatial modeling, the kappa index of agreement (KIA) representing the model’s simulation accuracy was used here to evaluate the model’s ability to simulate the spatial pattern of land use [52,53].”