more papers

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@@ -3,22 +3,49 @@ 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]](https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1433343)
+
 ### 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]](http://www.mdpi.com/2072-4292/9/12/1220)
+- **Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery** (2015), F. Hu et al. [[pdf]](http://www.mdpi.com/2072-4292/7/11/14680)
+- **Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network** (2017), G. Fu et al. [[pdf]](http://www.mdpi.com/2072-4292/9/5/498)
+- **Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection** (2016), H. Lyu et al. [[pdf]](http://www.mdpi.com/2072-4292/8/6/506)
 
 ### Google Earth Engine
 - **Google Earth Engine: Planetary-scale geospatial analysis for everyone** (2017), N. Gorelick et al. [[pdf]](https://www.sciencedirect.com/science/article/pii/S0034425717302900)
 
 ### Image Segmentation
+-- **Review of remote sensing image segmentation techniques** (2015), H. Kaur [[pdf]](https://pdfs.semanticscholar.org/71bd/3e87594a1d3467809e545cb3261e641fbac8.pdf)
 
 ### GEOBIA/OBIA
 - **Object based image analysis for remote sensing** (2010), T. Blaschke [[pdf]](https://www.sciencedirect.com/science/article/pii/S0924271609000884)
 - **Geographic Object-Based Image Analysis – Towards a new paradigm** (2014), T. Blaschke [[pdf]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3945831/)
 - **A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables** (2014), D. Clewley et al. [[pdf]](http://www.mdpi.com/2072-4292/6/7/6111/htm)
 
 ### Indices
+- **Monitoring vegetation systems in the great plains with erts** (1974), J. W. Rouse et al. [[pdf]](https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19740022614.pdf)
+- **A review of vegetation indices** (1996), B. Abdou et al. [[pdf]](https://www.researchgate.net/publication/236768837_A_review_of_vegetation_indices)
+- **NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space** (1996), B. Gao [[pdf]](https://www.sciencedirect.com/science/article/pii/S0034425796000673)
+- **Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications** (2017), J. Xue et al. [[pdf]](https://www.hindawi.com/journals/js/2017/1353691/)
+- **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]](https://www.researchgate.net/publication/260845342_A_new_agricultural_drought_monitoring_index_combining_MODIS_NDWI_and_day-night_land_surface_temperatures_A_case_study_in_China)
+- **Comparison of different vegetation indices for the remote assessment of green leaf area index of crops** (2011), A. Viña et al. [[pdf]](https://msu.edu/~vina/2011_RSE_GLAI.pdf)
 
 ### Change Detection
+- **Monitoring land-cover changes: a comparison of change detection techniques** (1999), J. F. Mas [[pdf]](https://pdfs.semanticscholar.org/e2c2/fa0ff875b08ca2f04590a60d04ab4c61121e.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]](https://www.researchgate.net/publication/222491847_Multivariate_Alteration_Detection_MAD_and_MAF_Postprocessing_in_Multispectral_Bitemporal_Image_Data_New_Approaches_to_Change_Detection_Studies)
+- **Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data** (2003), L. Yang et al. [[pdf]](http://web.pdx.edu/~nauna/articles/Yang_etal_2003.pdf)
+- **Rapid land use change after socio-economic disturbances: the collapse of the Soviet Union versus Chernobyl** (2011), P. Hostert et al. [[pdf]](http://iopscience.iop.org/article/10.1088/1748-9326/6/4/045201/meta)
 
 ### Surface Temperature
 - **Satellite-derived land surface temperature: Current status and perspectives** (2013), Z. Li et al.  [[pdf]](https://www.sciencedirect.com/science/article/pii/S0034425712004749)
+- **Online Global Land Surface Temperature Estimation from Landsat** (2017), D. Parastatidis et al. [[pdf]](Online Global Land Surface Temperature Estimation from Landsat)
+
+### Time Series and Trend Analysis
+- **Detecting trend and seasonal changes in satellite image time series** (2010), J. Verbesselt et al. [[pdf]](https://www.researchgate.net/publication/222561032_Detecting_trend_and_seasonal_changes_in_satellite_image_time_series)
+- **Detecting Change Dates from Dense Satellite Time Series Using a Sub-Annual Change Detection Algorithm** (2015), S. Cai et al. [[pdf]](http://www.mdpi.com/2072-4292/7/7/8705)
+
+## Other articles/tutorials
+### Deep Learning
+- **Deep Learning for Semantic Segmentation of Aerial Imagery** (2017), L. Fishgold et al. [[pdf]](https://www.azavea.com/blog/2017/05/30/deep-learning-on-aerial-imagery/)
+- **Super-Resolution on Satellite Imagery using Deep Learning, Part 1** (2016), P. Hagerty [[pdf]](https://medium.com/the-downlinq/super-resolution-on-satellite-imagery-using-deep-learning-part-1-ec5c5cd3cd2)
+-**Super-Resolution on Satellite Imagery using Deep Learning, Part 2** (2016), P. Hagerty [[pdf]](https://medium.com/the-downlinq/super-resolution-on-satellite-imagery-using-deep-learning-part-2-c9ce41dc0ee0)
+-**Super-Resolution on Satellite Imagery using Deep Learning, Part 3** (2017), P. Hagerty [[pdf]](https://medium.com/the-downlinq/super-resolution-on-satellite-imagery-using-deep-learning-part-3-2e2f61eee1d3)