Evaluation of ForestPA for VHR RS image classification using spectral and superpixel-guided morphological profiles

Alim Samat,Sicong Liu,Claudio Persello,Erzhu Li,Zelang Miao,Jilili Abuduwaili

doi:10.1080/22797254.2019.1565418

Abstract

ABSTRACT In very high resolution (VHR) remote sensing (RS) classification tasks, conventional pixel-based contextual information extraction methods such as morphological profiles (MPs), extended MPs (EMPs) and MPs with partial reconstruction (MPPR) with limited numbers, sizes and shapes of structural elements (SEs) cannot perfectly match all sizes and shapes of the objects in an image. To overcome such limitation, we introduce novel spatial feature extractors, namely, the superpixel-guided morphological profiles (SPMPs), where the superpixels are used as SEs in opening by reconstruction and closing by reconstruction operations. Moreover, to avoid possible side effects from unusual maximum and minimum values within superpixels, the mean pixel value of superpixels is adopted (SPMPsM). Additionally, new decision forest based on penalizing the attributes in previous trees, the ForestPA is introduced and evaluated through a comparative investigation on three VHR multi-/hyperspectral RS image classification tasks. Support vector machine and benchmark ensemble classifiers, including bagging, AdaBoost, MultiBoost, ExtraTrees, Random Forest and Rotation Forest, are adopted. The experimental results confirm the effectiveness and superior performances of the proposed SPMPs and SPMPsM relative to those of the MPs and MPPR. Moreover, ForestPA outperforms only bagging and is not suitable for learning from large numbers of samples with high dimensionality from the computational efficiency and classification accuracy perspective.

Highlights

In recent years, airborne and spaceborne multi/hyperspectral remote sensors have advanced in terms of spectral and spatial resolution, which makes the analysis of small spatial structures possible with unprecedented spatial details
Benchmark and widely accepted EL classifiers including bagging, AdaBoost, MultiBoostAB, ExtraTrees, random forest (RaF) and rotation forest (RoF) are considered with the recommended parameters, except that the numbers of iterations for AdaBoost and MultiBoostAB, and numbers of decision tree (DT) in other ensembles are set to the same value as the number of DTs in ForestPA for fair evaluation
We have presented the implementation details, analyzed the parameter sensitivity and presented a comprehensive validation of two novel spatial feature extractors, namely, superpixel-guided morphological profiles (SPMPs) and SPMPsM, where the latter contains the mean values of superpixels

Summary

Introduction

Airborne and spaceborne multi/hyperspectral remote sensors have advanced in terms of spectral and spatial resolution, which makes the analysis of small spatial structures possible with unprecedented spatial details. The necessity of spectral dimensionality reduction and the need for specific spectral-spatial classifiers, have been identified by the HR and VHR multi-/hyperspectral remote sensing (RS) image processing community (Fauvel, Tarabalka, Benediktsson, Chanussot, & Tilton, 2013; Plaza et al, 2009). Motivated by the ability of texture features to provide a quantitative description of image properties, including smoothness, roughness, symmetry and regularity, many texture extraction methods, such as statistical (gray-level co-occurrence matrix, GLCM), geometrical and structural approaches; Markov random field (MRF)and conditional random field (CRF)-model-based approaches; and signal processing (Gabor filter) approaches, have been examined for urban landcover mapping (Kasetkasem, Arora, & Varshney, 2005; Ma et al, 2017; Rajadell, García-Sevilla, & Pla, 2013; Zhang, Wang, Gong, & Shi, 2003).

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Results

Conclusion