Multiresolution Segmentation Research Articles

Object-oriented U-GCN for open-pit mining extraction from high spatial resolution remote-sensing images of complex scenes

ABSTRACT Precise extraction of open-pit mines is crucial for resource management and ecological and environmental dynamic monitoring. Current methods for extracting open-pit mines encounter challenges such as low accuracy and difficulty detecting complex scenes of open-pit mining. To address these issues, this paper proposes an object-oriented intelligent extraction method for complex mining scenes using Gaofen-2(GF-2) high-resolution remote-sensing images, which expresses the pixel-level features at the object level and utilizes the unique feature propagation and aggregation capabilities of the graph structure for the extraction of the mines. First, an object-oriented feature expression strategy is introduced to express multi-level pixel-level features as object-level features by constructing objects with appropriate multi-resolution segmentation parameters. This approach effectively reduces the impact of isolated pixel noise and outliers on classification results. Second, this paper proposes a U-GCN-based open-pit mining extraction method that combines the powerful multi-level feature extraction capabilities of U-GCN to propagate and aggregate information in a graph structure, effectively modelling spatial relationships between different objects. This method achieves high-precision extraction of open-pit mining areas. In experiments conducted on two study areas of varying scales, the F1 scores for open-pit mine extraction reached 93.32% and 83.06%. Comparative experiments demonstrate that the proposed object-oriented U-GCN method performs superiorly in terms of accuracy, stability and robustness across mining scenes with different levels of complexity. The proposed open-pit mine extraction method offers new insights and methodologies for current extraction practices.

A new approach to dust source mapping using visual interpretation and object-oriented segmentation of satellite imagery

The emission of dust particles, mainly from arid and semi-arid lands, as a result of climate change and human activities, is known to be a global issue. Identifying dust emission sources is the first key step in dealing with the hazardous consequences of this rising phenomenon. This study is an attempt to address one of the major challenges in mapping dust emission sources. Accordingly, an innovative approach based on visual interpretation of multi-temporal MODIS-Terra/Aqua imagery and object-oriented image segmentation techniques has been developed and implemented in the study areas of the Tigris and Euphrates basin and eastern Iran. This approach takes advantage of land surface characteristics (i.e., dust drivers), including geomorphology, soil, land use/cover, and land surface radiation, to attribute dust emission hotspots to their corresponding areas using multi-source remote sensing data. The results show that the multi-resolution segmentation algorithm with optimized parameters can identify homogeneous segments corresponding to dust emission sources in the study areas with an average spatial agreement of ∼92% compared to the reference areas. Our findings emphasize the robustness and generalizability of the proposed approach, and its capabilities can be used in a complementary way with visual interpretation of satellite images to map dust sources with high spatial accuracy.

Extraction of Brightness Values of Different Object from IRS & Landsat Satellite Image : Comparative Study

The main aim of this research is to find optimum segmentation parameters for extracting different spectral values of a different object of Land sat and IRS image. A relatively new segmentation approach, multiresolution segmentation, is being examined using two data sets (Land sat and IRS).

Agroforestry area mapping using medium resolution satellite data and object-based image analysis

Agroforestry is gaining increased attention within global policy processes and has been promoted as a strategy for working towards better food security, climate change adaptation and mitigation and livelihood resilience. With this gaining importance of agroforestry, site-specific studies are required to delineate different agroforestry systems and estimate the exact area under agroforestry. In this study remote sensing and GIS techniques were used for the estimation of area under agroforestry using medium-resolution satellite (sentinel 2A and 2B) data. Agroforestry area classification and estimation were done in e-Cognition developer software (10.0 version) through multiresolution segmentation and object-based image analysis (OBIA). The agroforestry area estimated in the erstwhile Warangal district was 3753 ha with an overall accuracy of 86% and a kappa coefficient of 0.84. The major agroforestry systems observed in the erstwhile Warangal district were eucalyptus, subabul, malabar neem, teak, sandalwood and red sanders.

Comparison of UAV and WorldView-3 Imagery for Mangrove Species Mapping in Mundupesisir Coastal, Cirebon

Abstract One of the efforts for monitoring and managing mangrove areas is mapping the mangrove areas at the species level. This aims to explore the potential and efforts for rehabilitation, as well as an inventory of the mangrove areas biodiversity. The study area was located in the mangrove Kasih Sayang area, Mundupesisir Village, Cirebon Regency, West Java. Remote sensing image data which have high-resolution can be utilized to more efficiently map mangrove species. The approach that can be used is the Geographic Object-Based Imagery Analysis (GEOBIA) method. This research aims to (1) map the mangrove species in Mundupesisir Village, Cirebon using Unmanned Aerial Vehicle (UAV) data and WorldView-3 imagery, and (2) compare the results and accuracy assessment values of mangrove species from UAV data and WorldView-3 imagery. UAV data (0.0179 m) has a visible image (RGB) band, while WorldView-3 is a multispectral image that has 8 bands (2 m) and a panchromatic band (0.5 m). Field data collection was obtained and collected at purposive random sampling to identify different species based on their physical characteristics. The GEOBIA approach used includes segmentation and classification processes. Multiresolution segmentation algorithm was used in the segmentation process. The classification used the Nearest Neighbor algorithm based on segmentation results. The results of the GEOBIA approach successfully mapped three dominant species in the mangrove Kasih Sayang area, Mundupesisir, Cirebon Regency, namely Avicennia marina, Rhizophora mucronata, and Acrostichum aureum.

MurSS: A Multi-Resolution Selective Segmentation Model for Breast Cancer.

Accurately segmenting cancer lesions is essential for effective personalized treatment and enhanced patient outcomes. We propose a multi-resolution selective segmentation (MurSS) model to accurately segment breast cancer lesions from hematoxylin and eosin (H&E) stained whole-slide images (WSIs). We used The Cancer Genome Atlas breast invasive carcinoma (BRCA) public dataset for training and validation. We used the Korea University Medical Center, Guro Hospital, BRCA dataset for the final test evaluation. MurSS utilizes both low- and high-resolution patches to leverage multi-resolution features using adaptive instance normalization. This enhances segmentation performance while employing a selective segmentation method to automatically reject ambiguous tissue regions, ensuring stable training. MurSS rejects 5% of WSI regions and achieves a pixel-level accuracy of 96.88% (95% confidence interval (CI): 95.97-97.62%) and mean Intersection over Union of 0.7283 (95% CI: 0.6865-0.7640). In our study, MurSS exhibits superior performance over other deep learning models, showcasing its ability to reject ambiguous areas identified by expert annotations while using multi-resolution inputs.

Damage detection on cooling tower shell based on model textures

Ensuring the structural integrity of cooling towers is paramount for safety and efficient operation. This paper presents a novel approach for detecting damage on cooling tower shells, utilising textures derived from laser scanning and close-range photogrammetry. The proposed method delves beyond the limitations of solely relying on colour information by harnessing the rich details embedded in various textures, including diffuse, normal, displacement, and occlusion. The study demonstrates the efficacy of this approach for identifying significant concrete damage. A Convolutional Neural Network (CNN) trained on diffuse textures successfully detects high damage instances with minimal misdetection. However, accurately pinpointing low damage, often manifesting as subtle cracks, and mimicking other patterns like air pores, ribbing, and colour variations, presents a formidable challenge. To tackle this challenge, the authors introduce a novel "composed raster layer" that merges information from multiple textures. This pre-processed layer amplifies the visual cues associated with low damage, facilitating its differentiation from similar patterns. While the current implementation employing multi-resolution segmentation and rule-based classification exhibits promising results, further optimization is acknowledged to refine the accuracy of low damage detection. The successful application of textures commonly used in rendering techniques underscores their remarkable potential for enhancing damage detection in civil engineering applications. While acknowledging limitations such as the analysis of a single cooling tower and the reliance on specific software for damage detection, the study proposes future research directions. This research holds significant implications for the field of civil engineering by offering a promising approach for automated and efficient damage detection on cooling tower shells.

QUALITY ASSESSMENT FOR MULTI-RESOLUTION SEGMENTATION AND SEGMENT-ANYTHING MODEL USING WORLDVIEW-3 IMAGERY

Abstract. Image segmentation, is of utmost importance in the disciplines of digital image processing, particularly remote sensing and computer vision, has seen an increasing demand for precise and efficient algorithms. This study focuses to conduct a comparative exploration of the segmentation capabilities of two sophisticated techniques namely, Multiresolution Segmentation (MRS) and Segment Anything Model (SAM), leveraging the high-resolution WorldView-3 (WV-3) satellite image. MRS adopts a hierarchical methodology, segmenting an image into various scales while retaining a profound understanding of its structure. Conversely, SAM employs a deep learning algorithm, prioritizing segment creation based on conceptual pixel similarity, irrespective of spatial adjacency. The WV-3 image, featuring diverse land cover elements like agricultural parcels, industrial structures, roads, red roofs, single trees, and water bodies, serves as the basis for assessing segmentation quality. Both methods are applied to the image, and their outcomes are individually evaluated against manually generated polygonal land use/cover objects. Segmentation quality metrics are employed for assessment. Results reveal MRS effectively preserves fine details and entity delineation, while SAM excels in capturing contextually similar regions. MRS outperforms SAM with a negligible discrepancy, yet SAM demonstrates superiority in the red roof object, achieving an Intersection over Union (IoU) value of 0.70 compared to MRS’s 0.49. MRS tends to generate numerous segments for an item, while SAM produces only one segment. Nevertheless, it is important to recognise that both algorithms have specific constraints in particular scenarios, such as excessive segmentation in areas with abundant texture or inadequate segmentation in areas with slight changes.

Classification of Rice Growth Phases Using the K-Nearest Neighbor Algorithm in the Irrigation area of Seulimeum Sub District, Aceh, Indonesia

Rice (Oryza sativa L.) is the main food commodity for most of Indonesia’s population. The existence of irrigation areas dramatically affects the water demand in each phase of rice growth. Due to a lack of remote sensing knowledge, classifying the rice growth phase is done manually. This study aims to classify rice growth phases using the K-Nearest Neighbor Algorithm in the irrigation area of the Seulimuem Sub-District. This research uses Google Earth satellite imagery in November 2021 through three stages: multi-resolution segmentation, guided classification, and accuracy testing. The results showed that the paddy and non-paddy fields are 1,425.56 ha and 6,581.70 ha, respectively. The classification results of rice plant growth phases consist of 4 phases, namely the inundation phase, the vegetative phase, the generative phase, and the fallow phase. Based on the accuracy assessment results in November 2021, the overall accuracy ranged from 90.65% to 92.92%, with a kappa index value of 0.85 to 0.87, categorizing it as “almost perfect.”

Detection of Benggang in Remote Sensing Imagery through Integration of Segmentation Anything Model with Object-Based Classification

Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas.

Extraction of Hani terraces based on Sentinel-2 and GF-2 images in Honghe prefecture, Yunnan province

Monitoring Hani terraces quickly and accurately using remote sensing technology is crucial for the protecting World Cultural Heritage Sites. However, single remote sensing image is affected by the mutual constraints of temporal and spatial resolution, making it difficult to concurrently integrate key phenological and spatial information for accurate extraction. In this study, GF-2 and Sentinel-2 images are used to extract terraces based on objected-based image analysis. Firstly, the spatial and phenological features of objects were obtained by multi-resolution segmentation using GF-2 image. Secondly, the optimal spatial and key phenological features were optimized by recursive feature elimination cross-validation and separation index, respectively. Finally, all optimized features were adopted to extract terraces using random forest (RF) and support vector machine (SVM) classifiers, respectively. Comparing deep learning and traditional machine learning methods, the proposed method using RF achieved the highest accuracy with Kappa coefficient and overall accuracy of 89.45 and 94.73%, respectively.

Mangrove individual tree detection based on the uncrewed aerial vehicle multispectral imagery

Mangroves are one of the most threatened and vulnerable ecosystems in the world. They dominate the coastal wetlands of tropical and subtropical regions. These forest areas are frequently difficult to access, typical methods of forest inventory are difficult undertakings. Thus, the acquisition of forest attributes through remote sensing, such as the validated individual tree detection (ITD) aim can support management and conservation proposals. This study comprehensively validated ITD from high-resolution multispectral uncrewed aerial vehicle (UAV) surveys, forest inventory data, multiresolution segmentation, mean shift segmentation and gaussian model the aim of quantifying the canopies in different areas of mangrove. The validations methods were based on the number plot size, the polygon canopy, and point canopy obtained according to simple sampling plan statistics. The results demonstrated that when using plot size inside the flight area to calibrate the ITD methods, the gaussian model and the segmentation methods can reach an acceptable quality limit (AQL) of 15%. On the other hand, this study suggests as alternative to ITD a tree like objects (TLO) method for flying through dense forests without forest inventory with AQL of 40%. This study is critical for conservation and management efforts because it applies the reliability of multispectral UAV surveys to assessing tree abundance in mangrove forests and can be applied to quantify biomass and blue carbon in dense forests.

Mangrove forest mapping from object-oriented multi-feature ensemble classification using Sentinel-2 images

Accurate mapping of mangrove forests is crucial for understanding their ecosystem function and developing effective management policies. However, the absence of an operational multi-feature fusion approach and an ensemble classification system restricts the achievement of this goal. This study aims to develop an object-oriented multi-feature ensemble classification scheme (OMEC). First, an enhanced mangrove spectral index (EMSI) is established by analyzing the spectral reflectance differences between mangrove forests and other land cover types. Sentinel-2 images are segmented into objects using the multi-resolution segmentation method. Then, spectral, textural, and geometric features are extracted, and these features (including EMSI) are inputted into the nearest neighbor classifier to implement mangrove classification. The experiment was conducted in three typical mangrove areas in China using Sentinle-2 images. The results demonstrate that EMSI exhibits good spectral separability for mangroves and performs well in the ensemble classification scheme. The overall accuracy of mangrove classification exceeds 90%, with a Kappa coefficient greater than 0.88. The object-oriented multi-feature ensemble classification scheme significantly improves accuracy and exhibits excellent performance. The method enhances the accuracy of mangrove classification, enriches the approach to mangrove remote sensing interpretation, and offers data support and scientific references for the restoration, management, and protection of coastal wetlands.

Semiautomatic Mapping of Center Pivot Irrigated Areas Using Sentinel-2 Images and GEOBIA Approach

Image analysis and feature extraction of remoted sensing data are significant for mapping irrigated agriculture areas as a source of information to improve water management and agricultural planning. This paper presents an image segmented base approach GEOBIA (Geographic Object-Based Image Analysis) to extract irrigated areas by Center Pivot Irrigation System (CPIS). This study suggests a semi-automated recognition of circular patterns for the mapping of irrigated regions by center pivots, using Sentinel -2 MSI images, 10 meters spatial resolution. A set of images from different seasons, humid and dry are used to maximize de CPIS’s occurrence. A multiresolution segmentation method was applied, and a large number of segment-based shape features was extracted and used as input to a feature selection procedure (shape descriptors: Area; Compactness; Circularity Factor; Length/Width; Radius of smallest enclosing ellipse; and Roundness). In addition, another shape descriptor “Circularity Factor” was developed in this research and played an important role during preliminaries classification processes. The accuracy assessment of preliminaries classifications has validated used the Circularity Factor together with the other chosen shape descriptors to reach better results to CPIS’s detection. Furthermore, 86.23% of the CPIS mapped in the classification process is in accordance with the ground truth map. This methodology can be used to map large areas in a relatively short time and provides a tool for monitoring irrigated areas.

Object-based crop classification in Hetao irrigation zone by using deep learning and region merging optimization

crop classification is conducive to precision agriculture. Due to the cost of high-resolution image collection, it is uneasy to conduct crop classification in remotely sensed scenes using deep networks, which have become increasingly popular in remote sensing. This work combines geographical-based image analysis (GEOBIA) with deep learning for crop classification in a small area. An image classifier network is designed by using multi-scale CNN and transformer modules. The network input is an image transformed from a segment obtained using multi-resolution segmentation (MRS). An iterative optimization framework is developed to correct the segments with under-segmentation errors (USE). Two scenes of high-resolution images are employed for the experiment. The proposed optimization algorithm leads to superior performance to competitors. By using the proposed classifier network as a baseline, the optimization approach can improve overall accuracy (OA) by 4.33 % and 1.29 % respectively for the first and second dataset.

Object-Based Integration Using Deep Learning and Multi-Resolution Segmentation in Building Extraction from Very High Resolution Satellite Imagery

Son yıllarda, kentsel alanlarda yapılan analizler ve değişimlerin tespitinin hızlı ve güvenilir şekilde gerçekleştirilmesi konusundaki çalışmalarda artış olmuştur. Bu doğrultuda, binaların sınıflandırılması bilgisayarlı görünün ön plana çıkan güncel konularından biridir. Birçok alanda olduğu gibi bu konuda da derin öğrenme mimarilerinin kullanımı trend uygulamalar arasındadır. Bina ayak izinin belirlenmesi amacıyla evrişimsel sinir ağları (ESA) kullanılarak semantik segmentasyon uygulamaları yaygınlaşmıştır. Ancak derin öğrenme ile segmentasyon işlemleri sonrası elde edilen tahmin görüntülerinde karşılaşılan problemlerin başında tuz-biber etkisiyle oluşmuş gürültüler gelmektedir. Bu çalışmada güncel evrişimsel sinir ağları (ESA) mimarilerinden olan U-Net ve SegNet algoritmalarının kullanımının, Nesne-Tabanlı Görüntü Analizinin (NTGA), Çoklu-Çözünürlüklü Bölütleme (ÇÇB) algoritmasıyla entegrasyonu önerilmiştir. Deneyler çok yüksek çözünürlüklü uydu görüntülerinden (Gaofen-2, Worldview-2 ve Ikonos) oluşan açık paylaşımlı Wuhan Üniversitesi Bina Çıkarımı Veriseti (WHUBED) üzerinde gerçekleştirilmiştir. Önerilen yaklaşım, genel doğruluk, F1 skor ve IoU metriklerinde, sadece ESA kullanımıyla elde edilen tahmin sonuçlarına göre iyileştirmeler sağlamıştır. Bina sınıflandırılması ile elde edilen haritalar karşılaştırılmalı görseller olarak son kısımda sunulmuştur.

Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey

The purpose of this study is to employ a remote sensing reconnaissance survey based on optimal segmentation parameters and an object-oriented random forest approach to the identification of possible terrestrial impact craters from the global 30-m resolution SRTM DEM. A dataset consisting of 94 confirmed and well-preserved terrestrial impact craters, 104 volcanic calderas, and 124 valleys were extracted from real-world surface features. For craters with different sizes, eight optimal scale parameters from 80 to 3000 have been identified using multi-resolution segmentation, where the scale parameters have a positive correlation (R2 = 0.78) with the diameters of craters. The object-oriented random forest approach classified the tested impact craters, volcanic calderas, and valleys with an overall accuracy of 88.4% and a Kappa coefficient of 0.8. The investigated terrestrial impact craters, in general, have relatively lower rim circularity, higher length-to-width ratio, and lower relief, slope, and elevation than volcanic calderas. The topographic characteristics can be explained by geological processes associated with the formation and post-deformation of impact craters. The excavation and ejection by initial impact and rebound of excavated materials contribute to low elevation. The post-impact deformation, including inward collapse and slump of unstable rims, weathering, erosion, and sediment deposition, further reduces elevation and relief and modifies shapes resulting in lower circularity and higher length-to-width ratio. Due to the resolution limitation of the source DEM data and the number of real-world samples, the model has only been validated for craters of 0.88 to 100 km in diameter, which can be generalized to explore undiscovered terrestrial impact craters using cloud computing with global datasets provided by platforms such as Google Earth Engine and Microsoft Planetary Computer.

Image Processing Techniques based Feature Extraction for Insect Damage Areas

Monitoring of forests is important for the diagnosis of insect damage to vegetation. Detection and monitoring of damaged areas facilitates the control of pests for practitioners. For this purpose, Unmanned Aerial Vehicles (UAVs) have been recently used to detect damaged areas. In order to distinguish damage areas from healthy areas on UAV images, it is necessary to extract the feature parameters of the images. Therefore, feature extraction is an important step in Computer Aided Diagnosis of insect damage monitored with UAV images. By reducing the size of the UAV image data, it is possible to distinguish between damaged and healthy areas from the extracted features. The accuracy of the classification algorithm depends on the segmentation method and the extracted features. The Grey-Level Co-occurrence Matrix (GLCM) characterizes areas texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships. In this paper, texture characteristics of insect damage areas were extracted from UAV images using with GLCM. The 3000*4000 resolution UAV images containing damaged and healthy larch trees were analyzed using Definiens Developer (e-Cognition software) for multiresolution segmentation to detect the damaged areas. In this analysis, scale parameters were applied as 500, shape 0.1, color 0.9 and compactness 0.5. As a result of segmentation, GLCM homogeneity, GLCM contrast and GLCM entropy texture parameters were calculated for each segment. When calculating the texturing parameters, neighborhoods in different angular directions (0,45,90,135) are taken into account. As a result of the calculations made by considering all directions, it was found that GLCM homogeneity values ranged between 0.08 - 0.2, GLCM contrast values ranged between 82.86 - 303.58 and GLCM entropy values ranged between 7.81 - 8.51. On the other hand, GLCM homogeneity for healthy areas varies between 0.05 - 0.08, GLCM contrast between 441.70 - 888.80 and GLCM entropy between 8.93 - 9.40. The study demonstrated that GLCM technique can be a reliable method to detection of insect damage areas from UAV imagery.

Spatial and Temporal Dynamics of Rangeland Ecosystem Services Across the Shazand Watershed, Iran

Human interventions and global environmental changes are increasing and threaten ecosystem services (ES) worldwide. Managing ES is a challenging task; it is essential to understand the temporal and spatial distribution of ES and the benefits derived by a human. However, ES variability in time and space has yet to be studied. Therefore, the current study investigates spatiotemporal variations of different ES indicators, individually and in combination, in the Shazand Watershed across three time periods: 1998, 2008, and 2016. Five indicators were selected to assess different dimensions of ES, including plant production, water provision, soil stability, maintenance of habitats and genetic diversity for plant and animal nurseries and reproduction, and recreation potential. We then used multiresolution segmentation to create a spatial composite indicator measuring the integration of the study indicators. SCI was used to describe spatiotemporal variations of ES through time at the subwatershed level. The results indicated that individual ES indicators display spatial variations across space and time. Moreover, comparing the spatial composite indicator across periods showed that all 24 subwatersheds could not supply more ES. However, the areas located in the central and the western part of the watershed appeared to have better ES conditions. According to the results, revising the current management plans and allocating environmental funding would benefit these areas. The results are applicable for future spatial planning of sustainable ecological conservation strategies for the Shazand Watershed and similar regions.

Combining OBIA, CNN, and UAV photogrammetry for automated avalanche deposit detection and characterization

Avalanche activities in the snow-covered mountainous slopes are very frequent and stochastically distributed over time and geographical extents. Conventional methods of avalanche occurrence reporting in these areas are inefficient and incomplete due to inhospitable weather and inaccessible topographic conditions. This paper presents a novel framework for the detection and analysis of avalanche deposits using UAV (Unmanned Aerial Vehicle) RGB images of snow-bound avalanche-prone areas. In the first part of the proposed framework, an OBIA-CNN (Object Based Image Analysis-Convolution Neural Network) method is applied to UAV images for the detection of avalanche deposits. Initially, the OBIA-based multi-scale multi-resolution segmentation technique is used for UAV image segmentation, then the classification of these image segments was performed using a CNN classifier to detect the avalanche deposits. The CNN-based classifier was created, trained, validated, and applied to generate the class probability maps of different classes present in the images. The post-classification refinements were then applied to the detected deposits. In the second step the detailed accuracy analysis of the detected deposits based on their count, area, size similarity, and shape similarity with respect to reference deposits has been carried out to establish the suitability of the proposed method. The avalanche deposits detected using the proposed method were found in good correlation with manually delineated deposits in terms of all four accuracy criteria. The precision of the model was found 1.0, the value of recall was 0.88 and the F-1 score was 0.93. The overall object shape matching accuracy of the detected deposits was also found high (0.92). The results of the proposed deposit detection method were also compared with the classification results of Support Vector Machine (SVM), Random Forest (RF), and UNET to evaluate the performance of the method. Finally, the surface area and snow volume-based characterization of the detected deposits was performed. The proposed framework will be useful for the automated detection of avalanche deposits and their characterization for the regions of specific interest. The study suggests that regular monitoring of avalanche activities using UAV images provides a good solution for the operational monitoring of important corridors of snowbound regions.