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

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

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

Retrieval of soil salinity from Sentinel-2 multispectral imagery

Soil salinity is a widespread environmental hazard and the main causes of land degradation and desertification, especially in arid and semi-arid regions. The first step in finding such a solution is providing accurate information about the severity and extent of the salinity spread in affected areas; this can be done by mapping the electrical conductivity (EC) of the soil. Utilizing the potential of high-resolution satellite imagery along with remote sensing techniques is a promising method to map salinity, as it allows for large-scale monitoring and provides high accuracy and efficiency. This paper, therefore, aims at assessing soil salinity by mapping the EC of soils, using satellite imagery from the newly launched Sentinel-2 satellite as well as Landsat-8 data. A field study was carried out using those data, and various salt features were extracted that relate the EC values of field samples to satellite-derived salt features. The study used two different regression approaches MLP and SVR. Additionally, two feature selection algorithms, GA and SFS, were implemented on the data to improve model performance. The study concludes that the proposed method for modeling salinity and the mapping of soil EC can be considered an effective approach for soil salinity monitoring

A new land-cover match-based change detection for hyperspectral imagery

The presence of phenomena such as earthquakes, floods and artificial human activities causes changes on the Earth’s surface. Change detection (CD) is an essential tool for the monitoring and managing of resources on local and global scales. Hyperspectral imagery can provide more detailed results for detecting changes in land-cover types. The main objective of this paper is to present a new, supervised CD method by combining similarity-based and distance-based methods to increase the efficiency of already existing CD approaches. The proposed method applies in two phases and uses three different algorithms, including image differencing, modified Z-score analysis and spectral angle mapper. The efficiency of the presented method is evaluated using Hyperion multi-temporal hyperspectral imagery. The receiver-operating characteristic curve index is used for assessing and comparing the results. The results clearly demonstrate the superiority of the proposed method for the detection and production of more accurate change maps. Furthermore, the proposed method is also able to detect changes with an accuracy of more than 96%, a false alarm rate lower than 0.03 and an area under the curve of about 0.986 in overall comparison to other conventional CD techniques. In addition, this method achieved an optimal threshold value with more rapid convergence

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

International audienceWildfires are one of the most destructive natural disasters that can affect our environment, with significant effects also on wildlife. Recently, climate change and human activities have resulted in higher frequencies of wildfires throughout the world. Timely and accurate detection of the burned areas can help to make decisions for their management. Remote sensing satellite imagery can have a key role in mapping burned areas due to its wide coverage, high-resolution data collection, and low capture times. However, although many studies have reported on burned area mapping based on remote sensing imagery in recent decades, accurate burned area mapping remains a major challenge due to the complexity of the background and the diversity of the burned areas. This paper presents a novel framework for burned area mapping based on Deep Siamese Morphological Neural Network (DSMNN-Net) and heterogeneous datasets. The DSMNN-Net framework is based on change detection through proposing a pre/post-fire method that is compatible with heterogeneous remote sensing datasets. The proposed network combines multiscale convolution layers and morphological layers (erosion and dilation) to generate deep features. To evaluate the performance of the method proposed here, two case study areas in Australian forests were selected. The framework used can better detect burned areas compared to other state-of-the-art burned area mapping procedures, with a performance of >98% for overall accuracy index, and a kappa coefficient of >0.9, using multispectral Sentinel-2 and hyperspectral PRISMA image datasets. The analyses of the two datasets illustrate that the DSMNN-Net is sufficiently valid and robust for burned area mapping, and especially for complex areas

A Sub-Pixel Multiple Change Detection Approach for Hyperspectral Imagery

One of the most important applications of remote sensing is change detection (CD). The accurate detection of changes is of great significance for the optimal management of available resources. This article presents an unsupervised ‘multiple-change detection’ method using multi-temporal hyperspectral imaging based on the integration of an unmixing technique, multi-resolution segmentation, similarity measure methods, and the Otsu algorithm. The proposed method is presented in the context of two main scenarios: the first scenario is hyperspectral change detection (HSCD) at the sub-pixel level with no ancillary data; and the second is the HSCD at the sub-pixel level based on ancillary data (high-resolution panchromatic (PAN) data). The main advantages of the proposed method are that it is unsupervised, easy to use, providing ‘multiple-change’ maps at the sub-pixel level with high accuracy. To evaluate the performance of the proposed method, real bi-temporal hyperspectral Hyperion images and high spatial resolution of PAN data belonging to the Advanced Land Imager (ALI) sensor with a variety of land cover classes were used. The results show the overall accuracy improved by >5%, and kappa coefficient improved by >0.13 with respect to the results obtained from their original resolution

Leveraging involution and convolution in an explainable building damage detection framework

ABSTRACTTimely and accurate building damage mapping is essential for supporting disaster response activities. While RS satellite imagery can provide the basis for building damage map generation, detection of building damages by traditional methods is generally challenging. The traditional building damage mapping approaches focus on damage mapping based on bi-temporal pre/post-earthquake dataset extraction information from bi-temporal images, which is difficult. Furthermore, these methods require manual feature engineering for supervised learning models. To tackle the abovementioned limitation of the traditional damage detection frameworks, this research proposes a novel building damage map generation approach based only on post-event RS satellite imagery and advanced deep feature extractor layers. The proposed DL based framework is applied in an end-to-end manner without additional processing. This method can be conducted in five main steps: (1) pre-processing, (2) model training and optimization of model parameters, (3) damage mapping generation, (4) accuracy assessment, and (5) visual explanations of the proposed method’s predictions. The performance of the proposed method is evaluated by two real-world RS datasets that include Haiti-earthquake and Bata-explosion. Results of damage mapping show that the proposed method is highly efficient, yielding an OA of more than 84%, which is superior to other advanced DL-based damage detection methods

TCD-Net: A Novel Deep Learning Framework for Fully Polarimetric Change Detection Using Transfer Learning

Due to anthropogenic and natural activities, the land surface continuously changes over time. The accurate and timely detection of changes is greatly important for environmental monitoring, resource management and planning activities. In this study, a novel deep learning-based change detection algorithm is proposed for bi-temporal polarimetric synthetic aperture radar (PolSAR) imagery using a transfer learning (TL) method. In particular, this method has been designed to automatically extract changes by applying three main steps as follows: (1) pre-processing, (2) parallel pseudo-label training sample generation based on a pre-trained model and fuzzy c-means (FCM) clustering algorithm, and (3) classification. Moreover, a new end-to-end three-channel deep neural network, called TCD-Net, has been introduced in this study. TCD-Net can learn more strong and abstract representations for the spatial information of a certain pixel. In addition, by adding an adaptive multi-scale shallow block and an adaptive multi-scale residual block to the TCD-Net architecture, this model with much lower parameters is sensitive to objects of various sizes. Experimental results on two Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) bi-temporal datasets demonstrated the effectiveness of the proposed algorithm compared to other well-known methods with an overall accuracy of 96.71% and a kappa coefficient of 0.82

A Convolutional Neural Network Method for Rice Mapping Using Time-Series of Sentinel-1 and Sentinel-2 Imagery

Rice is one of the most essential and strategic food sources globally. Accordingly, policymakers and planners often consider a special place in the agricultural economy and economic development for this essential commodity. Typically, a sample survey is carried out through field observations and farmers’ consultations to estimate annual rice yield. Studies show that these methods lead to many errors and are time-consuming and costly. Satellite remote sensing imagery is widely used in agriculture to provide timely, high-resolution data and analytical capabilities. Earth observations with high spatial and temporal resolution have provided an excellent opportunity for monitoring and mapping crop fields. This study used the time series of dual-pol synthetic aperture radar (SAR) images of Sentinel-1 and multispectral Sentinel-2 images from Sentinel-1 and Sentinel-2 ESA’s Copernicus program to extract rice cultivation areas in Mazandaran province in Iran. A novel multi-channel streams deep feature extraction method was proposed to simultaneously take advantage of SAR and optical imagery. The proposed framework extracts deep features from the time series of NDVI and original SAR images by first and second streams. In contrast, the third stream integrates them into multi-levels (shallow to deep high-level features); it extracts deep features from the channel attention module (CAM), and group dilated convolution. The efficiency of the proposed method was assessed on approximately 129,000 in-situ samples and compared to other state-of-the-art methods. The results showed that combining NDVI time series and SAR data can significantly improve rice-type mapping. Moreover, the proposed methods had high efficiency compared with other methods, with more than 97% overall accuracy. The performance of rice-type mapping based on only time-series SAR images was better than only time-series NDVI datasets. Moreover, the classification performance of the proposed framework in mapping the Shirodi rice type was better than that of the Tarom type