INCREMENTAL LEARNING METHOD FOR REMOTE SENSING IMAGE SEGMENTATION
As a powerful visual model, Incremental Learning Method (ILM) have demonstrated remarkable performance in various visual recognition problems and attracted considerable attention in recent years. However, due to the highly correlated bands and insufficient training samples of hyperspectral image data, it remains a challenging problem to effectively apply the ILN models on hyperspectral images. In this Matlab design, an efficient ILM and SVM architecture has been designed using the below-mentioned feature extraction methods to boost its discriminative capability for hyperspectral image classification, in which the original data is used as the input and the final ILN, SVM outputs are the predicted class-related results.
Feature Extraction Methods
Full trained AlexNet
Fine-tuned AlexNet
Pre-trained AlexNet
Full trained CaffeNet
Fine-tuned CaffeNet
Pre-trained CaffeNet
Full trained GoogLeNet
Fine-tuned GoogLeNet
Pre-trained GoogLeNet
Full trained VGGConvNets
Fine-tuned VGGConvNets
Pre-trained VGGConvNets
The proposed ILM is compared with SVM for the various feature extraction methods. The experiments on Zurich Dataset V1.0 have demonstrated using Matlab 2018a version and results show remote sensing image segmentation based on the segment classes: Roads, Buildings, Trees, Grass, Bare Soil, Water, Railways and Swimming pools
Reference Paper-1: Convolutional Neural Networks for Hyper Spectral Image Classification
Author’s Name: Shiqi Yu, Sen Jia, and ChunyanXu
Source: Elsevier-Neurocomputing
Year:2016
Reference Paper-2: Towards better Exploiting Convolutional Neural Networks for Remote Sensing
Author’s Name: Keiller Nogueira, Otavio A.B.Penatti, and Jefersson A.dos Santos
Source: Elsevier-Pattern Recognition
Year:2016
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SIMULATION VIDEO DEMO
