Join the Land use/cover (LUC) class attribute of the selected regular points with the image segments. Vector --> Data Management Tools --> Join Attributes by Location . Check the correctness of the C_ID classification field by means of visual interpretation of Google maps. Object-based classifier training

Object based image analysis is observed and used in the past few years. Its application has been extended in various fields, especially for forest mapping and Land use land cover classification [15] and [17]. Object-based, has emerged, and has generally had better success with narrow band and high spatial resolution data [26]. An object based image Object-based methods For treating landscape elements as objects, Earth Engine contains several methods. Specifically, when an image has distinct patches identified by unique pixel values, use image.connectedPixelCount() to compute the number of pixels in each patch and image.connectedComponents() to label each patch with a unique identifier. Image classification refers to the task of extracting information classes from a multiband raster image. The resulting raster from image classification can be used to create thematic maps. Depending on the interaction between the analyst and the computer during classification, there are two types of classification: supervised and unsupervised. and depth images which together can improve object recognition. Most current methods rely on very well-designed features for this new 3D modality. We in-troduce a model based on a combination of convolutional and recursive neural networks (CNN and RNN) for learning features and classifying RGB-D images. In our recent paper on an open source system for object based image classification [1] we mentioned linking with scikit-learn [2] to apply different classification algorithms. This post presents an example using Random Forests to give an idea of all the steps required. classifier = trainImageCategoryClassifier(imds,bag) return