Bahram Salehi

Object-Based Classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data

Land cover classification of very high resolution (VHR) imagery over urban areas is an extremely challenging task. Impervious land covers such as buildings, roads, and parking lots are spectrally too similar to be separated using only the spectral information of VHR imagery. Additional information, therefore, is required for separating such land covers by the classifier. One source of additional information is the vector data, which are available in archives for many urban areas. Further, the object-based approach provides a more effective way to incorporate vector data into the classification process as the misregistration between different layers is less problematic in object-based compared to pixel-based image analysis. In this research, a hierarchical rule-based object-based classification framework was developed based on a small subset of QuickBird (QB) imagery coupled with a layer of height points called Spot Height (SH) to classify a complex urban environment. In the rule-set, different spectral, morphological, contextual, class-related, and thematic layer features were employed. To assess the general applicability of the rule-set, the same classification framework and a similar one using slightly different thresholds applied to larger subsets of QB and IKONOS (IK), respectively. Results show an overall accuracy of 92% and 86% and a Kappa coefficient of 0.88 and 0.80 for the QB and IK Test image, respectively. The average producers’ accuracies for impervious land cover types were also 82% and 74.5% for QB and IK.

Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration

A vast portion of Newfoundland and Labrador (NL) is covered by wetland areas. Notably, it is the only province in Atlantic Canada that does not have a wetland inventory system. Wetlands are important areas of research because they play a pivotal role in ecological conservation and impact human activities in the province. Therefore, classifying wetland types and monitoring their changes are crucial tasks recommended for the province. In this study, wetlands in five pilot sites, distributed across NL, were classified using the integration of aerial imagery, Synthetic Aperture Radar, and optical satellite data. First, each study area was segmented using the object-based method, and then various spectral and polarimetric features were evaluated to select the best features for identifying wetland classes using the Random Forest algorithm. The accuracies of the classifications were assessed by the parameters obtained from confusion matrices, and the overall accuracies varied between 81% and 91%. Moreover, the average producer and user accuracies for wetland classes, considering all pilot sites, were 71% and 72%, respectively. Since the proposed methodology demonstrated high accuracies for wetland classification in different study areas with various ecological characteristics, the application of future classifications in other areas of interest is promising.

The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method

ABSTRACT Speckle noise significantly degrades the radiometric quality of PolSAR image and, consequently, decreases the classification accuracy. This article proposes a new speckle reduction method for PolSAR imagery based on an adaptive Gaussian Markov Random Field model. We also introduce a new span image, called pseudo-span, obtained by the diagonal elements of the coherency matrix based on the least square analysis. The proposed de-speckling method was applied to full polarimetric C-band RADARSAT-2 data from the Avalon area, Newfoundland, Canada. The efficiency of the proposed method was evaluated in 2 different levels: de-speckled images and classified maps obtained by the Random Forest classifier. In terms of de-speckling, the proposed method illustrated approximately 19%, 43%, 46%, and 50% improvements in equivalent number of looks values, in comparison with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. Also, improvements of approximately 19%, 9%, 55%, and 32% were obtained in the overall classification accuracy using de-speckled PolSAR image by the proposed method compared with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. This new adaptive de-speckling method illustrates to be an efficient approach in terms of both speckle noise suppression and details/edges preservation, while having a great influence on the overall wetland classification accuracy.

An Assessment of Simulated Compact Polarimetric SAR Data for Wetland Classification Using Random Forest Algorithm

ABSTRACT Synthetic aperture radar (SAR) compact polarimetry (CP) systems are of great interest for large area monitoring because of their ability to acquire data in a wider swath compared to full polarimetry (FP) systems and a significant improvement in information content compared to single or dual polarimetry (DP) sensors. In this study, we compared the potential of DP, FP, and CP SAR data for wetland classification in a case study located in Newfoundland, Canada. The DP and CP data were simulated using full polarimetric RADARSAT-2 data. We compared the classification results for different input features using an object-based random forest classification. The results demonstrated the superiority of FP imagery relative to both DP and CP data. However, CP indicated significant improvements in classification accuracy compared to DP data. An overall classification accuracy of approximately 76% and 84% was achieved with the inclusion of all polarimetric features extracted from CP and FP data, respectively. In summary, although full polarimetric SAR data provide the best classification accuracy, the results demonstrate the potential of RADARSAT Constellation Mission for mapping wetlands in a large landscape.

Well site extraction from Landsat-5 TM imagery using an object-and pixel-based image analysis method

Well sites, including both well pads and exploratory core holes, are small polygonal landscape disturbance features approximately one half to one hectare (0.5–1 ha) in area, resulting from oil and gas exploration activities. Automatic extraction and monitoring of such small features using remote-sensing technology at regional scales has always been desirable for wildlife habitat monitoring and environmental planning and modelling. Due to the vast disturbances of well sites in a province like Alberta, Canada, high-resolution imagery is not practical for well site extraction. For operational purposes, mid-resolution and cost-effective satellite imagery such as Landsat is the choice. However, automatic well site extraction using mid-resolution satellite imagery is a challenging task. Wells are typically less than three pixels in width and length in a Landsat multispectral image. Furthermore, the spectral contrast between the well site pixels and the surrounding areas is low due to vegetation regrowth and the spectral complexity of the surrounding environment. This article presents a novel methodology for automatic extraction of well sites from Landsat-5 TM imagery. The method combines both pixel- and object-based image analyses and contains three major steps: geometric enhancement, segmentation, and well site extraction. The method was applied to Landsat-5 TM images acquired over Fort McMurray, Alberta, Canada. For accuracy assessment, four regions of interest were selected and the results of the proposed automatic method were evaluated against visual inspection of the Landsat-8 pan-sharpened image. The method results in a total average correctness, completeness, and quality measures of about 80, 96, and 77%, respectively over the four sites. In addition, the method is very fast as an entire Landsat scene is processed in less than 10 minutes. The method is an operational approach for automatic detection of well sites over the entire province and can dramatically reduce the labour cost of manual digitization for monitoring and updating well site maps.

Wetland Classification Using Multi-Source and Multi-Temporal Optical Remote Sensing Data in Newfoundland and Labrador, Canada

ABSTRACT Newfoundland and Labrador (NL) is the only province in Atlantic Canada that does not have a wetland inventory system. As a consequence, both classifying and monitoring wetland areas are necessary for wetland conservation and human services in the province. In this study, wetlands in 5 pilot sites, distributed across NL, were classified using multi-source and multi-temporal optical remote sensing images. The procedures involved the application of an object-based method to segment and classify the images. To classify the areas, 5 different machine learning algorithms were examined. The results showed that the Random Forest (RF) algorithm in combination with an object-based approach was the most accurate method to classify wetlands. The average producer and user accuracies of wetland classes considering all pilot sites were 68% and 73%, respectively. The overall classification accuracies, which considered the accuracy of all wetland and non-wetland classes varied from 86% to 96% across all pilot sites confirming the robustness of the methodology despite the biological, ecological, and geographical differences among the study areas. Additionally, we assessed the effects of the tuning parameters on the accuracy of results, as well as the difference between pixel-based and object-based methods for wetland classification in this study.

Object-Based Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data

ABSTRACT Despite the fact that vast portions of Newfoundland and Labrador (NL) are covered by wetlands, currently there is no provincial inventory of wetlands in the province. In this study, we analyzed multi-temporal synthetic aperture radar (SAR) data for wetland classification at 4 pilot sites across NL. Object-based image analysis (OBIA) using a segmentation method based on optical data (RapidEye image in this study), and well-adjusted to SAR images, was first compared to pixel-based classification. Next, multi-date object-based wetland maps using the Random Forest classifier were compared to single-date classification. Finally, ratio and textural features were evaluated for wetland classification. The OBIA method demonstrated superior results, and the multi-date classification performed better than single-date classification with accuracies ranging from 75% to 95%. The multi-date results showed that the images acquired in August are the most appropriate for classifying wetlands, while the October images are of less value. Also, covariance matrix is a valuable feature set for wetland mapping. Moreover, ratio and textural features slightly increase the overall accuracy when the initial overall accuracy is relatively low. It can be concluded that multi-date SAR classification, with the proposed segmentation method, shows great potential for mapping wetlands and can be applied throughout the province.

Remote sensing for wetland classification: a comprehensive review

Wetlands are valuable natural resources that provide many benefits to the environment. Therefore, mapping wetlands is crucially important. Several review papers on remote sensing (RS) of wetlands have been published thus far. However, there is no recent review paper that contains an inclusive description of the importance of wetlands, the urgent need for wetland classification, along with a thorough explanation of the existing methods for wetland mapping using RS methods. This paper attempts to provide readers with an exhaustive review regarding different aspects of wetland studies. First, the readers are acquainted with the characteristics, importance, and challenges of wetlands. Then, various RS approaches for wetland classification are discussed, along with their advantages and disadvantages. These approaches include wetland classification using aerial, multispectral, synthetic aperture radar (SAR), and several other data sets. Different pixel-based and object-based algorithms for wetland classification are also explored in this study. The most important conclusions drawn from the literature are that the red edge and near-infrared bands are the best optical bands for wetland delineation. In terms of SAR imagery, large incidence angles, short wavelengths, and horizontal transmission and vertical reception polarization are best for detecting of herbaceous wetlands, while small incidence angles, long wavelengths, and horizontal transmission and reception polarization are appropriate for mapping forested wetlands.

Speckle filtering of Synthetic Aperture Radar images using filters with object-size-adapted windows

ABSTRACT Speckle degrades the radiometric quality of a Synthetic Aperture Radar (SAR) image. Previous methods for speckle reduction have used a fixed-size window for filtering the entire image. This, however, may not be effective for the entire image, as land covers of different sizes require different filtering windows. In this paper, a novel method is proposed by which each pixel in the image is filtered with a window appropriate for the size of object within it. The real in-phase and the imaginary quadrature components of the SAR images determine the best window size and the pixels in the intensity image are filtered using their own optimal windows. The proposed method is presented for both single- and multi-polarized SAR images, and the results of several common filters that were modified are presented. This approach is applied to two RADARSAT-2 images: one over San Francisco, California, USA and the other over St. John’s, Newfoundland and Labrador, Canada, producing results that were similar to, or outperformed, comparable filters while retaining details and suppressing speckle effectively. While the method was successful for single-look intensity data, it offers great potential for multi-look and amplitude data as well.

An efficient feature optimization for wetland mapping by synergistic use of SAR intensity, interferometry, and polarimetry data

Abstract Wetlands are home to a great variety of flora and fauna species and provide several unique environmental services. Knowledge of wetland species distribution is critical for sustainable management and resource assessment. In this study, multi-temporal single- and full-polarized RADARSAT-2 and single-polarized TerraSAR-X data were applied to characterize the wetland extent of a test site located in the north east of Newfoundland and Labrador, Canada. The accuracy and information content of wetland maps using remote sensing data depend on several factors, such as the type of data, input features, classification algorithms, and ecological characteristics of wetland classes. Most previous wetland studies examined the efficiency of one or two feature types, including intensity and polarimetry. Fewer investigations have examined the potential of interferometric coherence for wetland mapping. Thus, we evaluated the efficiency of using multiple feature types, including intensity, interferometric coherence, and polarimetric scattering for wetland mapping in multiple classification scenarios. An ensemble classifier, namely Random Forest (RF), and a kernel-based Support Vector Machine (SVM) were also used to determine the effect of the classifier. In all classification scenarios, SVM outperformed RF by 1.5–5%. The classification results demonstrated that the intensity features had a higher accuracy relative to coherence and polarimetric features. However, an inclusion of all feature types improved the classification accuracy for both RF and SVM classifiers. We also optimized the type and number of input features using an integration of RF variable importance and Spearman’s rank-order correlation. The results of this analysis found that, of 81 input features, 22 were the most important uncorrelated features for classification. An overall classification accuracy of 85.4% was achieved by incorporating these 22 important uncorrelated features based on the proposed classification framework.