santonugoswami / awesome-remote-sensing-papers

Selection of remote sensing papers

Awesome Remote Sensing Papers

A curated list of the best remote sensing papers by category

Machine Learning

• Implementation of machine-learning classification in remote sensing: an applied review (2018), A.E. Maxwell et al. [pdf]

Deep Learning

• Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study (2017), E. Guirado et al. [pdf]
• Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery (2015), F. Hu et al. [pdf]
• Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network (2017), G. Fu et al. [pdf]
• Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection (2016), H. Lyu et al. [pdf]
• ImageNet Classification with Deep Convolutional Neural Networks (2012), A. Krizhevsky et at. [pdf]
• Very Deep Convolutional Networks for Large-Scale Image Recognition (2015), K. Siminyan & A. Zisserman [pdf]
• Gradient-based Learning Applied to Document Recognition (1998), Y. LeCun et al. [pdf]

Image Segmentation

• Review of remote sensing image segmentation techniques (2015), H. Kaur [pdf]

GEOBIA/OBIA

• Object based image analysis for remote sensing (2010), T. Blaschke [pdf]
• Geographic Object-Based Image Analysis – Towards a new paradigm (2014), T. Blaschke [pdf]
• A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables (2014), D. Clewley et al. [pdf]

Indices

• Monitoring vegetation systems in the great plains with erts (1974), J. W. Rouse et al. [pdf]
• A review of vegetation indices (1996), B. Abdou et al. [pdf]
• NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space (1996), B. Gao [pdf]
• Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications (2017), J. Xue et al. [pdf]
• A new agricultural drought monitoring index combining MODIS NDWI and day-night land surface temperatures: A case study in China (2013), H. Sun et al. [pdf]
• Comparison of different vegetation indices for the remote assessment of green leaf area index of crops (2011), A. Viña et al. [pdf]

Change Detection

• Monitoring land-cover changes: a comparison of change detection techniques (1999), J. F. Mas [pdf]
• Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies (1998), A. A. Nielsen et al. [pdf]
• Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data (2003), L. Yang et al. [pdf]
• Rapid land use change after socio-economic disturbances: the collapse of the Soviet Union versus Chernobyl (2011), P. Hostert et al. [pdf]

Surface Temperature

• Satellite-derived land surface temperature: Current status and perspectives (2013), Z. Li et al. [pdf]
• Online Global Land Surface Temperature Estimation from Landsat (2017), D. Parastatidis et al. [pdf]
• Diversification of Land Surface Temperature Change under Urban Landscape Renewal: A Case Study in the Main City of Shenzhen, China (2017), Y. Liu et al. [pdf]

Time Series and Trend Analysis

• Detecting trend and seasonal changes in satellite image time series (2010), J. Verbesselt et al. [pdf]
• An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks (2010), C. Huang et al. [pdf]
• Detecting Change Dates from Dense Satellite Time Series Using a Sub-Annual Change Detection Algorithm (2015), S. Cai et al. [pdf]
• Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics (2015), T. Hermosilla et al. [pdf]
• A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series (2017), J.C. White et al. [pdf]
• Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review (2017) M. Hirschmugl et al. [pdf]
• Mapping tropical disturbed forests using multi-decadal 30 m optical satellite imagery (2019) Y. Wang et al. [pdf]

Tools

• Google Earth Engine: Planetary-scale geospatial analysis for everyone (2017), N. Gorelick et al. [pdf]
• Spring: Integrating remote sensing and gis by objectoriented data modelling (1996), G. Camara et al. [pdf]
• TerraLib: Technology in Support of GIS Innovation (2000) G. Camara et al. [pdf]
• OMT-G: An Object-Oriented Data Model for Geographic Applications (2001), K. A. V. Borges et al. [pdf]
• The e-sensing architecture for big earth observation data analysis (2018), G. Camara et al. [pdf]
• The KEA image file format (2013), P. Bunting et al. [pdf]

Other articles/tutorials/thesis

Deep Learning

• Deep Learning for Instance Segmentation of Agricultural Fields (2017), C. Rieke [pdf]
• Deep Learning for Semantic Segmentation of Aerial Imagery (2017), L. Fishgold et al. [pdf]
• Super-Resolution on Satellite Imagery using Deep Learning, Part 1 (2016), P. Hagerty [pdf]
• Super-Resolution on Satellite Imagery using Deep Learning, Part 2 (2016), P. Hagerty [pdf]
• Super-Resolution on Satellite Imagery using Deep Learning, Part 3 (2017), P. Hagerty [pdf]