Multi-Task Joint Sparse and Low-Rank Representation for the Scene Classification of High-Resolution Remote Sensing Image

Kunlun Qi,Wenxuan Liu,Chao Yang,Huayi Wu,Qingfeng Guan

doi:10.3390/rs9010010

Abstract

Scene classification plays an important role in the intelligent processing of High-Resolution Satellite (HRS) remotely sensed images. In HRS image classification, multiple features, e.g., shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS images. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structures across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

Highlights

With the rapid development of remote sensing techniques over recent years, High-ResolutionSatellite (HRS) images are becoming increasingly available enabling us to study earth observations in greater detail
Experimental results on the UC Merced dataset (UCM) and WHU-RS datasets indicate that the proposed MTJSLRC model is competitive with other feature combination methods for High-Resolution Satellite (HRS) image classification
This paper presents the Multi-Task Joint Sparse Representation Classification (MTJSLRC)

Summary

Introduction

With the rapid development of remote sensing techniques over recent years, High-ResolutionSatellite (HRS) images are becoming increasingly available enabling us to study earth observations in greater detail. Despite enhanced resolution, these details often suffer from the spectral uncertainty problem stemming from an increase of the intra-class variance and decrease of the inter-class variance [1], and the curse of dimensionality problem resulting from the small ratio between the number of training samples and features [2] Taking into account these characteristics, HRS image classification methods have evolved from pixel-oriented methods to object-oriented methods and achieved precise object recognition [3,4,5]. Object-oriented feature extraction methods cluster homogeneous pixels and take advantage of both local and global properties [6] These successful developments in feature extraction technologies for HRS satellite images have increased the usefulness of remote sensing applications in environmental and land resource management, security and defense issues, and urban planning, etc.

Objectives

Methods

Results

Conclusion