Improvements in Sample Selection Methods for Image Classification

Traditional image classification algorithms are mainly divided into unsupervised and supervised paradigms. In the first paradigm, algorithms are designed to automatically estimate the classes’ distributions in the feature space. The second paradigm depends on the knowledge of a domain expert to identify representative examples from the image to be used for estimating the classification model. Recent improvements in human-computer interaction (HCI) enable the construction of more intuitive graphic user interfaces (GUIs) to help users obtain desired results. In remote sensing image classification, GUIs still need advancements. In this work, we describe our efforts to develop an improved GUI for selecting the representative samples needed to estimate the classification model. The idea is to identify changes in the common strategies for sample selection to create a user-driven sample selection, which focuses on different views of each sample, and to help domain experts identify explicit classification rules, which is a well-established technique in geographic object-based image analysis (GEOBIA). We also propose the use of the well-known nearest neighbor algorithm to identify similar samples and accelerate the classification.

[1]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[2]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[3]  Germain Forestier,et al.  Knowledge-based region labeling for remote sensing image interpretation , 2012, Comput. Environ. Urban Syst..

[4]  Ian Witten,et al.  Data Mining , 2000 .

[5]  Marco A. Casanova,et al.  TerraLib: An Open Source GIS Library for Large-Scale Environmental and Socio-Economic Applications , 2008 .

[6]  Thomas Blaschke,et al.  Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[7]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[8]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[9]  Geoffrey J. Hay,et al.  Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline , 2008 .

[10]  Silvia Biasotti,et al.  What’s in an image? , 2005, The Visual Computer.

[11]  Josef Strobl,et al.  What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS , 2001 .

[12]  Leila Maria Garcia Fonseca,et al.  GeoDMA - Geographic Data Mining Analyst , 2013, Comput. Geosci..

[13]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .