Feature selection of various land cover indices for monitoring surface heat island in Tehran city using Landsat 8 imagery

Recently, scientists have been taking a great interest in Global warming issue, since the global surface temperature has been significantly increased all through last century. The surface heat island (SHI) refers to an urban area that has higher surface temperatures than its surrounding rural areas due to urbanization. In this paper, Tehran city is used as case study area. This paper tries to employ a quantitative approach to explore the relationship between land surface temperature and the most widespread land cover indices, and select proper (urban and vegetation) indices by incorporating supervised feature selection procedures using Landsat 8 imageries. In this regards, genetic algorithm is incorporated to choose best indices by employing kernel base one, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE = 0.9324, NRMSE = 0.2695 and R2 = 0.9315). First published online: 30 May 201

LAND COVER CHANGES DETECTION IN POLARIMETRIC SAR DATA USING ALGEBRA, SIMILARITY AND DISTANCE BASED METHODS

Monitoring and surveillance changes around the world need powerful methods, so detection, visualization, and assessment of significant changes are essential for planning and management. Incorporating polarimetric SAR images due to interactions between electromagnetic waves and target and because of the high spatial resolution almost one meter can be used to study changes in the Earth's surface. Full polarized radar images comparing to single polarized radar images use amplitude and phase information of the surface in different available polarization (HH, HV, VH, and VV). This study is based on the decomposition of full polarized airborne UAVSAR images and integration of these features with algebra method involves Image Differencing (ID) and Image Ratio (IR) algorithms with the mathematical nature and distance-based method involves Canberra (CA) and Euclidean (ED) algorithms with measuring distance between corresponding vector and similarity-based method involves Taminoto (TA) and Kulczynski (KU) algorithms with dependence corresponding vector for change detecting purposes on two real PolSAR datasets. Assessment of incorporated methods is implemented using ground truth data and different criteria for evaluating such as overall accuracy (OA), area under ROC curve (AUC) and false alarms rate (FAR). The output results show that ID, IR, and CA have superiority to detect changes comparing to other implemented algorithms. Also, numerical results show that the highest performance in two datasets has OA more than 90%. In other assessment criteria, mention algorithms have low FAR and high AUC value indices to detect changes in PolSAR images

Automatic mapping of burned areas using Landsat 8 time-series images in Google Earth engine: a case study from Iran

Due to the natural conditions and inappropriate management responses, large part of plains and forests in Iran have been burned in recent years. Given the increasing availability of open-access satellite images and open-source software packages, we developed a fast and cost-effective remote sensing methodology for characterizing burned areas for the entire country of Iran. We mapped the fire-affected areas using a post-classification supervised method and Landsat 8 time-series images. To this end, the Google Earth Engine (GEE) and Google Colab computing services were used to facilitate the downloading and processing of images as well as allowing for effective implementation of the algorithms. In total, 13 spectral indices were calculated using Landsat 8 images and were added to the nine original bands of Landsat 8. The training polygons of the burned and unburned areas were accurately distinguished based on the information acquired from the Iranian Space Agency (ISA), Sentinel-2 images, and Fire Information for Resource Management System (FIRMS) products. A combination of Genetic Algorithm (GA) and Neural Network (NN) approaches was then implemented to specify 19 optimal features out of the 22 bands. The 19 optimal bands were subsequently applied to two classifiers of NN and Random Forest (RF) in the timespans of 1 January 2019 to 30 December 2020 and of 1 January 2021 to 30 September 2021. The overall classification accuracies of 94% and 96% were obtained for these two classifiers, respectively. The omission and commission errors of both classifiers were also less than 10%, indicating the promising capability of the proposed methodology in detecting the burned areas. To detect the burned areas caused by the wildfire in 2021, the image differencing method was used as well. The resultant models were finally compared to the MODIS fire products over 10 sampled polygons of the burned areas. Overall, the models had a high accuracy in detecting the burned areas in terms of shape and perimeter, which can be further implicated for potential prevention strategies of endangered biodiversity.Peer ReviewedPostprint (published version

FULL POLARIMETRIC TIME SERIES IMAGE ANALYSIS FOR CROP TYPE MAPPING

Crop information and quality are not only fundamental for experts using spatial decision support systems but also have many applications in irrigation management, economic analysis for import or export, food safety, and achieving sustainable agriculture. Remote sensing is a cheap and fast way of reaching this goal. Full polarimetric SAR unlike optical sensors is an all-weather system providing geometrical and physical properties of the earth’s surface events. Due to the dynamic changes in crop properties through their phenological stages, crop type mapping has been challenging. As a result, accurate, reliable, and cost-effective crop type mapping using minimum data and processing has been the goal of the remote sensing and precision agriculture community. In this study, a new method based on time series analysis of full polarimetric SAR data combined with radar indices, polarimetric decompositions followed by the three αs extracted from H/A/α decomposition, and unsupervised H/α/Wishart classification bands as features generated from only 5 dates of RADARSAT CONSTELLATION MISSION 2 data were used for classification of crops. Applying random forest and cat boost algorithm as classifiers an accuracy of 87.4% and 75% was respectively achieved. indicating that both algorithms have promising results. Although the random forest algorithm had better results, the cat boost algorithm had less noise in each field and more homogenous farms were detected

Automatic road crack recognition based on deep learning networks from UAV imagery

Roads are one of the essential transportation infrastructures that get damaged over time and affect economic development and social activities. Therefore, accurate and rapid recognition of road damage such as cracks is necessary to prevent further damage and repair it in time. The traditional methods for recognizing cracks are using survey vehicles equipped with various sensors, visual inspection of the road surface, and recognition algorithms in image processing. However, performing recognition operations using these methods is associated with high costs and low accuracy and speed. In recent years, the use of deep learning networks in object recognition and visual applications has increased, and these networks have become a suitable alternative to traditional methods. In this paper, the YOLOv4 deep learning network is used to recognize four types of cracks transverse, longitudinal, alligator, and oblique cracks utilizing a set of 2000 RGB visible images. The proposed network with multiple convolutional layers extracts accurate semantic feature maps from input images and classifies road cracks into four classes. This network performs the recognition process with an error of 1% in the training phase and 77% F1-Score, 80% precision, 80% mean average precision (mAP), 77% recall, and 81% intersection over union (IoU) in the testing phase. These results demonstrate the acceptable accuracy and appropriate performance of the model in road crack recognition

Land cover changes detection in polarimetric SAR data using algebra, similarity and distance based methods

Monitoring and surveillance changes around the world need powerful methods, so detection, visualization, and assessment of significant changes are essential for planning and management. Incorporating polarimetric SAR images due to interactions between electromagnetic waves and target and because of the high spatial resolution almost one meter can be used to study changes in the Earth's surface. Full polarized radar images comparing to single polarized radar images use amplitude and phase information of the surface in different available polarization (HH, HV, VH, and VV). This study is based on the decomposition of full polarized airborne UAVSAR images and integration of these features with algebra method involves Image Differencing (ID) and Image Ratio (IR) algorithms with the mathematical nature and distance-based method involves Canberra (CA) and Euclidean (ED) algorithms with measuring distance between corresponding vector and similarity-based method involves Taminoto (TA) and Kulczynski (KU) algorithms with dependence corresponding vector for change detecting purposes on two real PolSAR datasets. Assessment of incorporated methods is implemented using ground truth data and different criteria for evaluating such as overall accuracy (OA), area under ROC curve (AUC) and false alarms rate (FAR). The output results show that ID, IR, and CA have superiority to detect changes comparing to other implemented algorithms. Also, numerical results show that the highest performance in two datasets has OA more than 90%. In other assessment criteria, mention algorithms have low FAR and high AUC value indices to detect changes in PolSAR images

SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM

In recent decades, global warming and sea level rise, population growth, and intensification of human activities, have directly affected the coasts and as such, their monitoring for the accretion and retreat are among the issues that are considered by the coastal countries. This study, compares two supervised classification algorithms for classifying Sentinel-2 satellite imagery for shoreline extraction. Median monthly images from 2020/01 to 2021/12 are taken and classified by Random Forest (RF) and Support Vector Machine (SVM) algorithms. By validating the maps, it is found that the RF algorithm has better accuracy and as such by averaging the accuracy of all maps, the overall accuracy (OA) values of 97.18% and the kappa coefficient (KC) of 0.97, and the mean overall accuracy and kappa coefficient of maps from SVM algorithm of 85.15% and 0.79, respectively, is obtained. After extracting the shorelines, the Digital Shoreline Analysis System (DSAS) is used to calculate the displacement rate. By calculating the Linear Regression Rate (LRR) factor, it is found that in 91% of transects (166 transects) we see the shoreline retreat to land. In 54% of them, the average rate of the retreat is 5.42 meters per year and in only 9% (16 transects) we see the accretion towards the sea

AUTOMATIC ROAD CRACK RECOGNITION BASED ON DEEP LEARNING NETWORKS FROM UAV IMAGERY

Roads are one of the essential transportation infrastructures that get damaged over time and affect economic development and social activities. Therefore, accurate and rapid recognition of road damage such as cracks is necessary to prevent further damage and repair it in time. The traditional methods for recognizing cracks are using survey vehicles equipped with various sensors, visual inspection of the road surface, and recognition algorithms in image processing. However, performing recognition operations using these methods is associated with high costs and low accuracy and speed. In recent years, the use of deep learning networks in object recognition and visual applications has increased, and these networks have become a suitable alternative to traditional methods. In this paper, the YOLOv4 deep learning network is used to recognize four types of cracks transverse, longitudinal, alligator, and oblique cracks utilizing a set of 2000 RGB visible images. The proposed network with multiple convolutional layers extracts accurate semantic feature maps from input images and classifies road cracks into four classes. This network performs the recognition process with an error of 1% in the training phase and 77% F1-Score, 80% precision, 80% mean average precision (mAP), 77% recall, and 81% intersection over union (IoU) in the testing phase. These results demonstrate the acceptable accuracy and appropriate performance of the model in road crack recognition

Comparative Study of Intrinsic Dimensionality Estimation and Dimension Reduction Techniques on Hyperspectral Images Using K-NN Classifier

Nowadays, hyperspectral remote sensors are readily available for monitoring the Earth’s surface with high spectral resolution. The high-dimensional nature of the data collected by such sensors not only increases computational complexity but also can degrade classification accuracy. To address this issue, dimensionality reduction (DR) has become an important aid to improving classifier efficiency on these images. The common approach to decreasing dimensionality is feature extraction by considering the intrinsic dimensionality (ID) of the data. A wide range of techniques for ID estimation (IDE) and DR for hyperspectral images have been presented in the literature. However, the most effective and optimum methods for IDE and DR have not been determined for hyperspectral sensors, and this causes ambiguity in selecting the appropriate techniques for processing hyperspectral images. In this letter, we discuss and compare ten IDE and six DR methods in order to investigate and compare their performance for the purpose of supervised hyperspectral image classification by using K-nearest neighbor (K-NN). Due to the nature of K-NN classifier that uses different distance metrics, a variety of distance metrics were used and compared in this procedure. This letter presents a review and comparative study of techniques used for IDE and DR and identifies the best methods for IDE and DR in the context of hyperspectral image analysis. The results clearly show the superiority of the hyperspectral signal subspace identification by minimum, second moment linear, and noise-whitened Harsanyi–Farrand–Chang estimators, also the principal component analysis and independent component analysis as DR techniques, and the norm L1 and Euclidean distance metrics to process hyperspectral imagery by using the K-NN classifier