Development of Land Cover Classification Model Using AI Based FusionNet Network

[1]  Xiaoguang Jiang,et al.  Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image , 2012 .

[2]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[3]  Anton van den Hengel,et al.  Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  C. Blasi,et al.  The concept of land ecological network and its design using a land unit approach , 2008 .

[5]  Pablo M. Granitto,et al.  Deep learning for plant identification using vein morphological patterns , 2016, Comput. Electron. Agric..

[6]  Haiyan Guan,et al.  Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters , 2020, ISPRS Journal of Photogrammetry and Remote Sensing.

[7]  Farid Melgani,et al.  Land-Cover Classification of Remotely Sensed Images Using Compressive Sensing Having Severe Scarcity of Labeled Patterns , 2015, IEEE Geoscience and Remote Sensing Letters.

[8]  Shihua Li,et al.  Object-Oriented Method Combined with Deep Convolutional Neural Networks for Land-Use-Type Classification of Remote Sensing Images , 2019, Journal of the Indian Society of Remote Sensing.

[9]  S. Rahman Six decades of agricultural land use change in Bangladesh: Effects on crop diversity, productivity, food availability and the environment, 1948–2006 , 2010 .

[10]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  José Cristóbal Riquelme Santos,et al.  A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks , 2019, Remote. Sens..

[12]  Chao Liu,et al.  Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning , 2017, Journal of Mountain Science.

[13]  Martha C. Anderson,et al.  Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources , 2012 .

[14]  Josef Strobl,et al.  Segmentation and Object-Based Image Analysis , 2010 .

[15]  Gérard Dedieu,et al.  Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2 , 2015, Remote. Sens..

[16]  Jun Yang,et al.  A Multitarget Land Use Change Simulation Model Based on Cellular Automata and Its Application , 2014 .

[17]  Vijay S. Rajpurohit,et al.  Systematic analysis of satellite image-based land cover classification techniques: literature review and challenges , 2019 .

[18]  Gang Fu,et al.  Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network , 2017, Remote. Sens..

[19]  Amy Loutfi,et al.  Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks , 2016, Remote. Sens..

[20]  Nikhil R. Pal,et al.  Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Sukyoung Hong,et al.  Farmland Use Mapping Using High Resolution Images and Land Use Change Analysis , 2012 .

[22]  K. Oost,et al.  An assessment of the global impact of 21st century land use change on soil erosion , 2017, Nature Communications.

[23]  Philip W. Gassman,et al.  Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions , 2008 .

[24]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[25]  A. Kamilaris,et al.  A review of the use of convolutional neural networks in agriculture , 2018, The Journal of Agricultural Science.

[26]  Liangpei Zhang,et al.  An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Wei Cao,et al.  Satellite-Observed Energy Budget Change of Deforestation in Northeastern China and its Climate Implications , 2015, Remote. Sens..

[28]  D. I. Sensuse,et al.  Cattle Race Classification Using Gray Level Co-occurrence Matrix Convolutional Neural Networks , 2015 .

[29]  A Study on Object-Based Image Analysis Methods for Land Cover Classification in Agricultural Areas , 2012 .

[30]  Brian P. Salmon,et al.  Multiview Deep Learning for Land-Use Classification , 2015, IEEE Geoscience and Remote Sensing Letters.