Classification Methods For Remotely Sensed Data

By S. Lowe. 2016

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Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms (2020, methodology): “CART is a simple binary decision tree classifier and works using a pre-defined threshold [18], while RF is an ensemble classifier based on the combination of multiple CART-like trees.”
A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing (2020, background): “environ ents [10], and airborne syste s [11,12], in this paper, e provide a co prehensive study of the ost r pres ntative forest fire d tection syst s, focusing on those that use optical re ot sensing, as el as digital i age processing [13] nd classification t chniques [14].”
Support vector machines in remote sensing: A review (2011, unknown): “In the remote sensing field a good starting point would be a textbook by Tso and Mather (2009) that provides a review of the entire field of classification methods for remotely sensed data, including SVMs.”