Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data

Abstract

Object-based image analysis (OBIA) has been widely used for land use and land cover (LULC) mapping using optical and synthetic aperture radar (SAR) images because it can utilize spatial information, reduce the effect of salt and pepper, and delineate LULC boundaries. With recent advances in machine learning, convolutional neural networks (CNNs) have become state-of-the-art algorithms. However, CNNs cannot be easily integrated with OBIA because the processing unit of CNNs is a rectangular image, whereas that of OBIA is an irregular image object. To obtain object-based thematic maps, this study developed a new method that integrates object-based post-classification refinement (OBPR) and CNNs for LULC mapping using Sentinel optical and SAR data. After producing the classification map by CNN, each image object was labeled with the most frequent land cover category of its pixels. The proposed method was tested on the optical-SAR Sentinel Guangzhou dataset with 10 m spatial resolution, the optical-SAR Zhuhai-Macau local climate zones (LCZ) dataset with 100 m spatial resolution, and a hyperspectral benchmark the University of Pavia with 1.3 m spatial resolution. It outperformed OBIA support vector machine (SVM) and random forest (RF). SVM and RF could benefit more from the combined use of optical and SAR data compared with CNN, whereas spatial information learned by CNN was very effective for classification. With the ability to extract spatial features and maintain object boundaries, the proposed method considerably improved the classification accuracy of urban ground targets. It achieved overall accuracy (OA) of 95.33% for the Sentinel Guangzhou dataset, OA of 77.64% for the Zhuhai-Macau LCZ dataset, and OA of 95.70% for the University of Pavia dataset with only 10 labeled samples per class.

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

“…It is, however, still arguable how well these DL algorithms perform against ensemble algorithms and SVM for the purpose of urban LULC classification. This is particularly true in the case of LULC classification using a GEOBIA approach, as it can be difficult to combine GEOBIA with some popular DL algorithms, e.g., convolutional neural networks (CNNs) [17,18].…”

Revealing Kunming’s (China) Historical Urban Planning Policies Through Local Climate Zones

“…To date, the WUDAPT Level 0 LCZ mapping community effort is evolving rapidly as demonstrated by a large number of developments such as a more stringent accuracy assessment procedure [41], the introduction of novel remote sensing information and learning/classification techniques [49,50], in-depth comparisons with local administrative datasets [44], applications in health-risk studies [51], comparisons with land-surface temperature dynamics [31,33,52], and the upscaling of city-wide maps to whole continents such as Europe [40]. This work contributes to these advances by providing a dynamic map for Kunming for different periods in time (2005, 2011 and 2017) and by interpreting the land-cover changes in terms of policy and planning regulations.…”

Object-based Method for Identifying New Constructions around Water Reservoirs: Preliminary Results

“…This larger dataset will allow us to assess and to improve the proposed approach in a systematic manner. As part of this ongoing work, we will compare the proposed approach to some state-of-the-art, such as [15], [16], [8], [9].…”