Change Detection of Amazonian Alluvial Gold Mining Using Deep Learning and Sentinel-2 Imagery

Kimijima S., Sakakibara M., Nagai M.

The rapid growth of artificially constructed mining camps has negatively impacted the camps’ surrounding environment and the informal communities that have developed inside the camps. However, artisanal and small-scale gold mining (ASGM) is generally informal, illegal, and unregulated; thus, transformations of the mining activities and potential social-environmental problems resulting from these changes are not revealed. This study assesses the transformation of mining activities in camp-type ASGM sectors in Gorontalo, Indonesia, during 2014–2020 using remotely sensed data, such as Landsat series, nighttime light, and precipitation data obtained through Google Earth Engine. Results show that the combined growth of the built-up areas increased 4.8-fold, and their annual mean nighttime light increased 3.8-fold during 2014–2019. Furthermore, diverse increases in the sizes of area and nighttime light intensity were identified from the mining camps. Among the studied camps, since 2017, Motomboto camp 3 showed a particularly rapid change in activity regardless of the season of the year. Hence, these approaches are capable of identifying rapid transformations in the mining activities and provide significant insight into the socio-environmental problems originating from the closed and vulnerable camp-based ASGM sector. Our results also contribute to developing rapid and appropriate interventions and strengthening environmental governance.

Polk S.L., Murphy J.M.

Clustering algorithms partition a dataset into groups of similar points. The primary contribution of this article is the Multiscale Spatially-Regularized Diffusion Learning (M-SRDL) clustering algorithm, which uses spatially-regularized diffusion distances to efficiently and accurately learn multiple scales of latent structure in hyperspectral images (HSI). The M-SRDL clustering algorithm extracts clusterings at many scales from an HSI and outputs these clusterings' variation of information-barycenter as an exemplar for all underlying cluster structure. We show that incorporating spatial regularization into a multiscale clustering framework corresponds to smoother and more coherent clusters when applied to HSI data and leads to more accurate clustering labels.

Yuan X., Shi J., Gu L.

• Summarize deep learning methods for semantic segmentation of remote sensing images. • Identify three major challenges faced by researchers. • Summarize the innovative development to address them. Semantic segmentation of remote sensing imagery has been employed in many applications and is a key research topic for decades. With the success of deep learning methods in the field of computer vision, researchers have made a great effort to transfer their superior performance to the field of remote sensing image analysis. This paper starts with a summary of the fundamental deep neural network architectures and reviews the most recent developments of deep learning methods for semantic segmentation of remote sensing imagery including non-conventional data such as hyperspectral images and point clouds. In our review of the literature, we identified three major challenges faced by researchers and summarize the innovative development to address them. As tremendous efforts have been devoted to advancing pixel-level accuracy, the emerged deep learning methods demonstrated much-improved performance on several public data sets. As to handling the non-conventional, unstructured point cloud and rich spectral imagery, the performance of the state-of-the-art methods is, on average, inferior to that of the satellite imagery. Such a performance gap also exists in learning from small data sets. In particular, the limited non-conventional remote sensing data sets with labels is an obstacle to developing and evaluating new deep learning methods.

Camalan S., Mahmood H., Binol H., Araújo A.L., Santos-Silva A.R., Vargas P.A., Lopes M.A., Khurram S.A., Gurcan M.N.

Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as “suspicious” and “normal” by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed method’s feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis.

Bounliyong P., Itaya T., Arribas A., Watanabe Y., Wong H., Echigo T.

Cordeiro M.C., Martinez J., Peña-Luque S.

Continuous monitoring of water surfaces is essential for water resource management. This study presents a nonparametric unsupervised automatic algorithm for the identification of inland water pixels from multispectral satellite data using multidimensional clustering and a high-performance subsampling approach for large scenes. Clustering analysis is a technique that is used to identify similar samples in a multidimensional data space. The spectral information and derived indices were used to characterize each scene pixel individually. A machine learning approach with random subsampling and generalization through a Naive Bayes classifier was also proposed to make the application of complex algorithms to large scenes feasible. Accuracy was evaluated using an independent dataset that provides water bodies in 15 Sentinel-2 images over France acquired in different seasons and that covers a large range of water bodies and water colour types. The validation dataset covers a water surface of more than 1200 km2 (approximately 12 million pixels) including over 80,000 water bodies outlined using a semiautomatic active learning method, which were manually revised. The classification results were compared to the water pixel classification using three of the major Level 2A processors (MAJA, Sen2Cor and FMask) and two of the most common thresholding techniques: Otsu and Canny-edge. An input mask was used to remove coastal waters, clouds, shadows and snow pixels. Water pixels were identified automatically from the clustering process without the need for ancillary or pretrained data. Combinations using up to three water indices (Modified Normalized Difference Water Index-MNDWI, Normalized Difference Water Index-NDWI and Multiband Water Index-MBWI) and two reflectance bands (B8 and B12) were tested in the algorithm, and the best combination was NDWI-B12. Of all the methods, our method achieved the highest mean kappa score, 0.874, across all tested scenes, with a per-scene kappa ranging from 0.608 to 0.980, and the lowest mean standard deviation of 0.091. Standard Otsu's thresholding had the worst performance due to the lack of a bimodal histogram, and the Canny-edge variation achieved an overall kappa of 0.718 when used with the MNDWI. For water masks provided by generic processors, FMask outperformed MAJA and Sen2Cor and obtained an overall kappa of 0.764. In-depth analysis shows a quick drop in performance for all of the methods in identifying water bodies with a surface area below 0.5 ha, but the proposed approach outperformed the second best method by 34% in this size class.

Gerson J.R., Topp S.N., Vega C.M., Gardner J.R., Yang X., Fernandez L.E., Bernhardt E.S., Pavelsky T.M.

Artificial lake expansion and high mercury loading synergistically increase mercury contamination risk from gold mining.

Hoeser T., Bachofer F., Kuenzer C.

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

Chen L., Zhang D., Li P., Lv P.

In recent years, image processing methods based on convolutional neural networks (CNNs) have achieved very good results. At the same time, many branch techniques have been proposed to improve accuracy. Aiming at the change detection task of remote sensing images, we propose a new network based on U-Net in this paper. The attention mechanism is cleverly applied in the change detection task, and the data-dependent upsampling (DUpsampling) method is used at the same time, so that the network shows improvement in accuracy, and the calculation amount is greatly reduced. The experimental results show that, in the two-phase images of Yinchuan City, the proposed network has a better antinoise ability and can avoid false detection to a certain extent.

Zeng Y., Yang X., Fang N., Shi Z.

China is facing the challenge of the uncertain impacts of large-scale afforestation on regional water resources. However, the effects of vegetation cover changes on the variation in surface water at the regional scale are still controversial. Here, we focused on the 0.9 million km2 vegetation restoration region in China, where the highest significant vegetation cover changes on the earth. Multi-source remote sensing data were used to describe the characteristics of seasonality and transition of surface water and to analyse the causes of surface water changes from climate, vegetation cover and other human factors. Our results show that the annual maximum NDVI of Northeast region (NE) and Loess Plateau region (LP) increased significantly from 0.74 to 0.85 and 0.49 to 0.62 from 2000 to 2015, respectively. Meanwhile, permanent water, as a vital component of surface water, exhibited net increases of 695.6 km2 and 119.4 km2 in NE and LP from 2000 to 2015, respectively. The extension in permanent water and the implementation of ecological projects exhibited highly consistent spatiotemporal patterns. Statistical analysis indicated that vegetation cover is an important factor in controlling permanent water changes. Human activities such as building dams and reservoirs are also an important explanatory variable for permanent water increases. The newly built dams contributed 43% in NE and 25% in LP to the increase in permanent water. In addition, although climatic factors were not the main factor influencing permanent water, precipitation significantly affected the total surface water in NE. These findings have potential implications for understanding surface water and forest dynamics and formulating regional development plans in the vegetation restoration region in China.

Khelifi L., Mignotte M.

Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which are frequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of change detection DL studies. Then, we focus on deep learning-based change detection methodologies for remote sensing images by giving a general overview of the existing methods. Specifically, these deep learning-based methods were classified into three groups; fully supervised learning-based methods, fully unsupervised learning-based methods and transfer learning-based techniques. As a result of these investigations, promising new directions were identified for future research. This study will contribute in several ways to our understanding of deep learning for change detection and will provide a basis for further research. Some source codes of the methods discussed in this paper are available from: https://github.com/lazharkhelifi/deeplearning_changedetection_remotesensing_review.

Mohan A., Singh A.K., Kumar B., Dwivedi R.

Landslide, one of the most critical natural hazards, is caused due to specific compositional slope movement. In the past decades, due to inflation of urbanized area and climate change, a compelling expansion in landslide prevalence took place which is also termed as mass/slope movement and mass wasting, causing extensive collapse around the world. The principal reason for its pursuance is a reduction in the internal resistance of soil and rocks, classified as a slide, topple, fall, and flow. Slopes can be differentiated based on earth material and the nature of its movements. The downward flow of landslides occurs due to excessive rainfall, snowmelt, earthquake, volcanic eruption, and so on. This review article revisits the conventional approaches for identification of landslides, predicting future risk, associated with slope failures, followed by emphasizing the advantages of modern geospatial techniques such as aerial photogrammetry, satellite remote sensing images (ie, panchromatic, multispectral, radar images), Terrestrial laser scanning, and High-Resolution Digital Elevation Model (HR-DEM) in updating landslide inventory maps. Machine learning techniques like Support Vector Machine, Artificial neural network, deep learning has been extensively used with geographical data producing effective results for assessment of natural hazard/resources and environmental research. Based on recent studies, deep learning is a reliable tool addressing remote sensing challenges such as trade-off in imaging system producing poor quality investigation, in addition, to expedite consequent task such as image recognition, object detection, classification, and so on. Conventional methods, like pixel and object-based machine learning methods, have been broadly explored. Advanced development in deep learning technique like CNN (Convolutional neural network) has been extensively successful in information extraction from an image and has exceeded other traditional approaches. Over the past few years, minor attempts have been made for landslide susceptibility mapping using CNN. In addition, small sample sizes for training purpose will be major drawback and notably remarkable while using deep learning techniques. Also, assessment of the model's performance with diverse training and testing proportion other than commonly utilized ratio, that is, 70/30 needs to be explored further. The review article briefly highlights the remote sensing methods for landslide detection using machine learning and deep learning.

Hoeser T., Kuenzer C.

Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and rapidly developing field mainly driven by advances in computer vision (CV). To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019. Thereby, we highlight the connections between the most important CNN architectures and cornerstones coming from CV in order to alleviate the evaluation of modern DL models. Furthermore, we briefly outline the evolution of the most popular DL frameworks and provide a summary of datasets in EO. By discussing well performing DL architectures on these datasets as well as reflecting on advances made in CV and their impact on future research in EO, we narrow the gap between the reviewed, theoretical concepts from CV and practical application in EO.

Camalan S., Niazi M.K., Moberly A.C., Teknos T., Essig G., Elmaraghy C., Taj-Schaal N., Gurcan M.N.

Acute infections of the middle ear are the most commonly treated childhood diseases. Because complications affect children’s language learning and cognitive processes, it is essential to diagnose these diseases in a timely and accurate manner. The prevailing literature suggests that it is difficult to accurately diagnose these infections, even for experienced ear, nose, and throat (ENT) physicians. Advanced care practitioners (e.g., nurse practitioners, physician assistants) serve as first-line providers in many primary care settings and may benefit from additional guidance to appropriately determine the diagnosis and treatment of ear diseases. For this purpose, we designed a content-based image retrieval (CBIR) system (called OtoMatch) for normal, middle ear effusion, and tympanostomy tube conditions, operating on eardrum images captured with a digital otoscope. We present a method that enables the conversion of any convolutional neural network (trained for classification) into an image retrieval model. As a proof of concept, we converted a pre-trained deep learning model into an image retrieval system. We accomplished this by changing the fully connected layers into lookup tables. A database of 454 labeled eardrum images (179 normal, 179 effusion, and 96 tube cases) was used to train and test the system. On a 10-fold cross validation, the proposed method resulted in an average accuracy of 80.58% (SD 5.37%), and maximum F1 score of 0.90 while retrieving the most similar image from the database. These are promising results for the first study to demonstrate the feasibility of developing a CBIR system for eardrum images using the newly proposed methodology.

Pahlevan N., Smith B., Schalles J., Binding C., Cao Z., Ma R., Alikas K., Kangro K., Gurlin D., Hà N., Matsushita B., Moses W., Greb S., Lehmann M.K., Ondrusek M., et. al.

Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in various aquatic ecosystems with broad ranges of trophic levels have long been a complex undertaking. Here, we introduce a machine-learning model, the Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in inland and coastal waters. The model is trained and validated using a sizeable database of co-located Chla measurements (n = 2943) and in situ hyperspectral radiometric data resampled to simulate the Multispectral Instrument (MSI) and the Ocean and Land Color Imager (OLCI) onboard Sentinel-2A/B and Sentinel-3A/B, respectively. Our performance evaluations of the model, via two-thirds of the in situ dataset with Chla ranging from 0.2 to 1209 mg/m3 and a mean Chla of 21.7 mg/m3, suggest significant improvements in Chla retrievals. For both MSI and OLCI, the mean absolute logarithmic error (MAE) and logarithmic bias (Bias) across the entire range reduced by 40–60%, whereas the root mean squared logarithmic error (RMSLE) and the median absolute percentage error (MAPE) improved two-to-three times over those from the state-of-the-art algorithms. Using independent Chla matchups (n

Huang K., Iversen L.L., Fensholt R., Gominski D., Brandt M., Mugabowindekwe M., Strange N., Bendixen M.

Saputra M.R., Bhaswara I.D., Nasution B.I., Ern M.A., Husna N.L., Witra T., Feliren V., Owen J.R., Kemp D., Lechner A.M.

Pacheco A.D., Nascimento J.A., Ruiz-Armenteros A.M., da Silva Junior U.J., Junior J.A., de Oliveira L.M., Melo dos Santos S., Filho F.D., Pessoa Mello Galdino C.A.

The uncontrolled expansion of mining activities has caused severe environmental impacts in semi-arid regions, endangering fragile ecosystems and water resources. This study aimed to propose a decision-making model to identify land use and land cover changes in the semi-arid region of Pernambuco, Brazil, caused by mining through a spatiotemporal analysis using high-resolution images from the PlanetScope satellite constellation. The methodology consisted of monitoring and evaluating environmental impacts using the k-Nearest Neighbors (kNN) algorithm, spectral indices (Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and hydrological data, covering the period from 2018 to 2023. As a result, a 3.28% reduction in vegetated areas and a 6.62% increase in urban areas were identified over five years, suggesting landscape transformation, possibly influenced by the expansion of mining and development activities. The application of kNN yielded an Overall Accuracy (OA) greater than 99% and a Kappa index of 0.98, demonstrating the effectiveness of the adopted methodology. However, challenges were encountered in distinguishing between constructions and bare soil, with the Jeffries–Matusita distance (JMD) analysis indicating a value below 0.34, while the similarity between water and vegetation highlights the need for more comprehensive training data. The results indicated that between 2018 and 2023, there was a marked degradation of vegetation and a significant increase in built-up areas, especially near water bodies. This trend reflects the intense human intervention in the region and reinforces the need for public policies aimed at mitigating these impacts, as well as promoting environmental recovery in the affected areas. This approach proves the potential of remote sensing and machine learning techniques to effectively monitor environmental changes, reinforcing strategies for sustainable management in mining areas.

Xie Z., Li K., Jiang J., Yang J., Qiao X., Yuan D., Nie C.

Timana-Mendoza C., Reyes-Calderon A., Venail P., Britzke R., Santa-Maria M.C., Araujo-Flores J.M., Silman M.R., Fernandez L.E.

AbstractThe expansion of artisanal and small-scale gold mining (ASGM) in the Madre de Dios region of the Peruvian Amazon has transformed primary forests into a novel wetland complex of thousands of abandoned mining ponds. Despite their ecological relevance, post-mining recovery of these systems remains understudied, particularly regarding fish biodiversity and recolonization. In this study, we evaluate fish community richness and composition in mining ponds of different dimensions, years post abandonment, and degree of pulse flood connectivity using traditional collection-based methods and environmental DNA (eDNA) with the 12S and COI markers. We compared these two methods of biodiversity inventory and contrasted results from ASGM waterbodies with those obtained from nearby pristine oxbow lakes. Overall, we registered more fish richness at all sites using eDNA vs traditional methods, especially with the 12S marker. We identified 14 and 13 unique genera using traditional methods and eDNA, respectively, with 40 genera detected by both approaches, evidencing their complementarity. Notably, we found that the degree of pulse flooding connectivity was the main predictor of species richness among the abandoned mining ponds (p-value < 0.05). We registered 11 to 22, 23 to 71, and 56 morphospecies in non-flooded mining ponds, pulse flooded mining ponds and nearby oxbow lakes, respectively. Furthermore, the fish community composition of mining ponds most influenced by pulse flooding were similar to that of pristine lakes. Our findings highlight the role of hydrological connectivity in ecological recovery within mining-impacted wetlands. Future restoration efforts should enhance aquatic connectivity to accelerate recovery in post-mining environments.

Gong X.

Addressing these challenges requires innovative monitoring techniques that can accurately assess the impact on land use and land cover. To address these issues, this study introduces a Semantic Web-enabled framework combined with superpixel-based image segmentation for effective environmental monitoring. Leveraging aerial imagery and artificial intelligence to monitor these effects, employing a Simple Linear Iterative Clustering (SLIC) algorithm for image segmentation and a combination of classifiers (Support Vector Machine, SVM; Random Forest, RF; and Naive Bayes, NB) for land change detection. The effectiveness of this Semantic Web-enabled approach is demonstrated by its ability to accurately identify areas affected by ASGM, with robust statistical metrics including a kappa coefficient of 0.7616, an F1 score of 0.8806, and a Jaccard index of 0.798. These results underscore the method's capability in providing detailed insights into land cover changes, thereby serving as a significant tool for environmental monitoring and aiding in policy formulation.

Becerra M., Villa L., Puzzi Nicolau A., Herndon K.E., Novoa S., Martín-Arias V., Dyson K., Walker K.A., Tenneson K.R., Saah D.

Abstract Peru’s Southeastern Amazon deforestation trends can be attributed to alluvial gold mining. Illegal mining occurring in forestry concessions, national parks, and the territories of Indigenous People Organizations is of particular concern. We present a methodology to create near real-time alerts of deforestation caused by alluvial gold mining. A time series of Sentinel-1 Synthetic Aperture Radar (SAR) data from February to December 2022 is created in Google Earth Engine (GEE) and assessed using Morton Canty’s Omnibus Q-test change detection algorithm. Resulting detections are validated with high-resolution optical imagery from Planet NICFI’s monthly basemaps and Planet Scope daily imagery. The alerts identify the location and timing of large areas (group pixels of <1 ha) of forest loss due to gold mining activities within buffer zones of indigenous territories and protected areas. The overall accuracy of the forest loss analysis conducted with this change detection method was 99.98%, based on an independent accuracy assessment (table 2). This effort has resulted in a public web platform that displays the location of near real time alerts, so Peruvian enforcement agencies can more effectively allocate resources and staff to addressing active illegal mining operations. These results demonstrate the applicability of open-source SAR data to monitor forest loss over areas where cloud cover is more persistent and to improve tools that deliver timely, critical information to decision-makers. Future applications of our method could expand this approach to other drivers of deforestation.

Altunel A.O., Çelik D.A.

Swiftly and reliably establishing a spatially and geometrically correct land-cover map of any region is rather important in natural resource planning for conservation and utilization. JAXA’s PALSAR2/PALSAR/JERS-1 Mosaic and Forest / Non-forest maps, which as the name suggested, have specifically focused on global forest cover since 2007, benefiting from L-band SAR imagery. ESRI Land-cover, on the other hand, owing to exceptional Sentinel-2 imagery, has produced rather detailed land-cover maps including a distinct forest class. In this particular study, coverages of 2017–2020 readied by both institutions, utilizing the aforementioned imageries, were questioned on yearly basis against a rather detailed geodatabase which is still-in-effective use by two of the current regional directorates of forestry, Kastamonu and Sinop in Türkiye, utilizing long adopted accuracy metrics (user, producer and overall accuracies). When all year coverages were concerned, the best overall accuracies were held with 82% in 2017 ESRI land-cover and 83% in 2017 PALSAR-FNF. Both datasets yielded relatively good results in the forest class when user accuracies were investigated. ESRI land-covers managed more than 87% across all four years, while PALSAR-FNFs produced 84.33% in 2020 as the highest scoring year. As for producer accuracies, PALSAR-FNFs produced over 89% across all year coverages, while ESRI produced 84% in 2017 as the highest scoring year. It is worth noting that the ESRI land-covers had better compliance with the compartment boundaries of the reference geodatabase.

Nugroho U.C., Susantoro T.M., Kushardono D., Nugroho G., Setiawan H.L., Suliantara, Ichsan N.

Yu W., Zhang X., Gloaguen R., Zhu X.X., Ghamisi P.

Nursamsi I., Phinn S.R., Levin N., Luskin M.S., Sonter L.J.

Artisanal and small-scale mining (ASM) significantly influences the socio-economic development of many low-to-middle-income countries, albeit sometimes at the expense of environmental and human health. Characterized by its labor-intensive extraction from confined (

Shkolnyi D., Magritsky D., Chalov S.

Yang M., Yang J., Mao H., Zheng C.

Change detection based on optical image processing plays a crucial role in the field of damage assessment. Although existing damage scene change detection methods have achieved some good results, they are faced with challenges, such as low accuracy and slow speed in optical image change detection. To solve these problems, an image change detection approach that combines infrared polarization imaging with a fast principal component analysis network (Fast-PCANet) is proposed. Firstly, the acquired infrared polarization images are analyzed, and pixel image blocks are extracted and filtered to obtain the candidate change points. Then, the Fast-PCANet network framework is established, and the candidate pixel image blocks are sent to the network to detect the change pixel points. Finally, the false-detection points predicted by the Fast-PCANet are further corrected by region filling and filtering to obtain the final binary change map of the damage scene. Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. The experimental results show that the proposed Fast-PCANet method improves the PCC and the Kappa coefficient of infrared polarization images over infrared intensity images by 6.77% and 13.67%, respectively. Meanwhile, the inference speed can be more than seven times faster. The results verify that the proposed approach is effective and efficient for the change detection task with infrared polarization imaging. The study can be applied to the damage assessment field and has great potential for object recognition, material classification, and polarization remote sensing.

Paheding S., Saleem A., Siddiqui M.F., Rawashdeh N., Essa A., Reyes A.A.

AbstractIn recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, and precision agriculture have leveraged the merits of the deep learning era. Particularly, deep learning has significantly improved the analysis of remote sensing images, with a continuous increase in the number of researchers and contributions to the field. The high impact of deep learning development is complemented by rapid advancements and the availability of data from a variety of sensors, including high-resolution RGB, thermal, LiDAR, and multi-/hyperspectral cameras, as well as emerging sensing platforms such as satellites and aerial vehicles that can be captured by multi-temporal, multi-sensor, and sensing devices with a wider view. This study aims to present an extensive survey that encapsulates widely used deep learning strategies for tackling image classification challenges in remote sensing. It encompasses an exploration of remote sensing imaging platforms, sensor varieties, practical applications, and prospective developments in the field.

Ponnuviji N.P., Nirmala G., Kokila M.L., Priyadharshini S.I.

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing approaches. This research contributes to the enhancement of secure and efficient cloud-based image retrieval systems, addressing modern challenges in data management and security.