Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution

Zhiyong Lv,Wenzhong Shi,Jón Benediktsson,Xiaojuan Ning

doi:10.3390/rs8121023

Abstract

Land cover classification using very high spatial resolution (VHSR) imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover classification. In this study, a new object-based image filter using topology and feature constraints is proposed, where an object is considered as a central object and has irregular shapes and various numbers of neighbors depending on the nature of the surroundings. First, multi-scale segmentation is used to generate a homogeneous image object and extract the corresponding vectors. Then, topology and feature constraints are proposed to select the adjacent objects, which present similar materials to the central object. Third, the feature of the central object is smoothed by the average of the selected objects’ feature. This proposed approach is validated on three VHSR images, ranging from a fixed-wing aerial image to UAV images. The performance of the proposed approach is compared to a standard object-based approach (OO), object correlative index (OCI) spatial feature based method, a recursive filter (RF), and a rolling guided filter (RGF), and has shown a 6%–18% improvement in overall accuracy.

Highlights

Very high spatial resolution (VHSR) remote sensing imagery, such as aerial images and unmanned aerial vehicle (UAV) images, reveal ground details, including texture, geometry, and topology, and provide an outstanding visual performance [1]
A new approach called OFTF was proposed to improve the performance of very high spatial resolution (VHSR) image classification
Experiments were conducted on three real VHSR images to show the effectiveness of the proposed approach

Summary

Introduction

Very high spatial resolution (VHSR) remote sensing imagery, such as aerial images and unmanned aerial vehicle (UAV) images, reveal ground details, including texture, geometry, and topology, and provide an outstanding visual performance [1]. Compared with the classification of high spatial resolution hyperspectral remote sensing images , the classification of remote sensing images with a high spatial resolution but a relatively low spectral resolution (such as images obtained by airborne or UAV) has become challenging. Given the improvement in spatial resolution, several zones may appear too small and heterogeneous when a VHSR image is processed by multi-scale segmentation. These zones may be meaningless relative to the classes of interest. A high intra-class and a low inter-class variability reduce the separability of different land cover classes in the spectral domain [1,5,6]

Methods

Results

Discussion

Conclusion