Image Segmentation and Classification with SLIC Superpixel and Convolutional Neural Network in Forest Context

Abstract

Unmanned aerial vehicles (UAVs) are platforms suitable for obtaining information utilizing sensors in a great variety of areas and enviroments, thus in this context, this paper objective was to identify trees in images collected using UAV high-resolution imagery, with the digital approach of superpixel segmentation and convolutional neural networks. A forest environment was analyzed in the form of an orthomosaic that was produced using 423 images. Superpixels were generated using the Simple Linear Iterative Clustering (SLIC) method, considering only two classes for the classification: trees and background. The generation of superpixels (segmentation) and classification were performed considering the configurations: 2000, 3000 and 4000 segments, sigma 5 and compactness 10 for SLIC and 100% transfer of learning. For the purposes of classification, the deep convolutional networks were adopted through ResNet-50 architecture, the weights of this network were previously trained in an Image bank and later the bank of images of superpixels underwent a fine-tuning. The experiments obtained a maximum classification accuracy of 89% with 3000 superpixels distinction between canopies and Background.