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Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type based on High Spatial Resolution Aerial images

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REMOTE SENSING 2024年第1期16卷 31页

作者： Li, Shiou Fei, Xianyun Chen, Peilong Wang, Zhen Gao, Yajun Cheng, Kai Wang, Huilong Zhang, Yuanzhi Jiangsu Ocean Univ Sch Geomatics & Marine Informat Lianyungang 222002 Peoples R China Lianyungang Forestry Tech Guidance Stn Lianyungang 222005 Peoples R China Chinese Acad Sci Key Lab Lunar & Deep Space Explorat Natl Astron Observ Beijing 100101 Peoples R China Univ Chinese Acad Sci Sch Astron & Space Sci Beijing 100049 Peoples R China

The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of object-based image analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

关键词： object-based image analysis deep learning mountain vegetation type classification high spatial aerial remote sensing images

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Machine-learning algorithms for land use dynamics in Lake Haramaya Watershed, Ethiopia

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2022年第3期8卷 3719-3736页

作者： Woldemariam, Gezahegn Weldu Tibebe, Degefie Mengesha, Tesfamariam Engida Gelete, Tadele Bedo Ethiopian Space Sci & Technol Inst ESSTI Entoto Observ & Res Ctr EORC Dept Remote Sensing & Applicat Res & Dev POB 33679 Addis Ababa Ethiopia Addis Ababa Univ POB 1176 Addis Ababa Ethiopia Haramaya Univ Sch Geog & Environm Studies Geoinformat Sci Program POB 138 Dire Dawa 3220 Ethiopia

Natural and human-induced drivers within the constraints of multiple socioeconomic and political conditions have intensified the extent of land use and land cover (LULC) change at different scales. Hence, up-to-date spatial and statistical information about LULC change is of utmost importance for the development and implementation of appropriate Earth resource management strategies and monitoring environmental changes. With the increased availability of remotely sensed data from various Earth observation satellite and continuous advances in computing technology, these pieces of information are commonly retrieved based on automated digital image classification algorithms. However, in-depth quantitative evaluation of different classification algorithms is critical to select an efficient classifier or multiple ways to obtain more accurate land use maps. Taking selected hydrological catchments of the Lake Haramaya Watershed in the East Hararghe Ethiopian highland as an example, we statistically compared the land use classification performance of three nonlinear machine-learning algorithms (MLAs), namely: support vector machine (SVM), random forest (RF), and artificial neural network (ANN) and object-based image analysis (OBIA) using open-source Landsat-8 satellite images. At the same time, the classification accuracy was assessed using sample points from high spatial resolution Google-Earth Pro image. The confusion matrix analysis has indicated that the generated land use maps had a divergent classification accuracy. However, the SVM achieved the highest overall classification accuracy (94%) compared to the RF (92%) and the ANN (89%). On the contrary, the OBIA classifier achieved the least overall accuracy (75%) compared to the former. Thus, the results presented here reveal that though all supervised MLAs can be considered robust classifiers, the SVM algorithm was found to be the best classifier to improve the classification accuracy at an individual land use class lev

关键词： Artificial neural network Earth observation satellite object-based image analysis LULC change Machine-learning algorithms Random forest Support vector machine

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Remote sensing of large wood in high-resolution satellite imagery: Design of an automated classification work-flow for multiple wood deposit types

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EARTH SURFACE PROCESSES AND LANDFORMS 2021年第12期46卷 2333-2348页

作者： Sendrowski, Alicia Wohl, Ellen Colorado State Univ Dept Geosci Ft Collins CO 80523 USA Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA

Wood researchers increasingly rely on remote-sensing products to augment field information about wood deposits in river corridors. The availability of very high-resolution (<1 m) satellite imagery makes capturing wood over greater spatial extents possible, but previous studies have found difficulty in automatically extracting wood deposits due to the challenge in distinguishing wood from spectrally similar corridor features such as sand. We also lack knowledge on the spectral properties of different wood deposit types in multiple depositional environments. In this work, we explore image classification work-flows for four wood deposit types in three North American environments: in-channel jams deposited in the Tatshenshini River in Alaska, USA;a wood raft on the Slave River in Northwest Territories, Canada;and wood deposited along a lakeshore and coastal embayment in the Mackenzie River Delta in Northwest Territories, Canada. We compare classification results of object-based and pixel-based image analysis with supervised [support vector machine (SVM)] and unsupervised (ISO clustering) classifiers. We evaluate several accuracy assessment parameters and achieve overall classification accuracies of 65-99%, showing automated image classification is a possible approach for analysing wood across larger areas. We also find that wood sensitivity in the classification ranged from 0 to 95%, indicating that some techniques are better suited to wood capture than others. We find that supervised classification produced more accurate wood maps, though there is large variation in classification outcomes across environments related to spatial arrangement of wood in the landscape. We discuss the influence of depositional environment on classification and provide recommendations for designing a wood classification work-flow.

关键词： depositional environments large wood object-based image analysis remote sensing river delta

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Semantic Unsupervised Change Detection of Natural Land Cover With Multitemporal object-based analysis on SAR images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第7期59卷 5494-5514页

作者： Amitrano, Donato Guida, Raffaella Iervolino, Pasquale Univ Napoli Federico II Dept Elect & Informat Technol Engn I-80125 Naples Italy Univ Napoli Ingn Elettron & Telecomunicaz I-80125 Naples Italy Surrey Space Ctr Dept Elect Engn Guildford GU2 7XH Surrey England

Change detection is one of the most addressed topics in the remote sensing community. When performed on synthetic aperture radar images, the most critical issues are as follows: 1) the labeling of the identified changing patterns and 2) the scarce robustness of classic pixel-based approaches based on threshold segmentation of an appropriate change index, which tend to fail when multiple changes are present in the study area. In this work, a new methodology for unsupervised change detection in vegetation canopy is presented. It overcomes these limitations by exploiting multitemporal geographical object-based image analysis with the aim to make the intrinsic semantic of data emerge and direct the processing toward the identification of precise classes of changes through dictionary-based preclassification and fuzzy combination of class-specific information layers. The proposed methodology has been tested in ten different experiments covering agriculture and clear-cut deforestation applications. The results, validated against literature methods, highlighted the superiority of the proposed approach, which was quantitatively assessed in terms of standard classification quality parameters. On agriculture experiments, it allowed for an average increase in the detection accuracy of about 11x0025;with respect to the best performing literature method, with an increment of the false alarm rate in the order of 0.5x0025;. In case of deforestation, the registered detection accuracy was comparable to that achieved by the literature, while the most significant benefit was the reduction, of more than one-third, of the number of detected false deforestation patterns. Overall, the main characteristics of the proposed architecture are the robustness and the lack of any supervision, which makes it very well-suited for operational scenarios.

关键词： Vegetation mapping Biomass Semantics Synthetic aperture radar Agriculture image segmentation Radar polarimetry Change detection classification multitemporal synthetic aperture radar (SAR) object-based image analysis

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Salt Marsh Species Classification and Soil Property Modeling Using Multiple Remote Sensors

Salt Marsh Species Classification and Soil Property Modeling...

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作者： Nicholson, Heather M. Florida Atlantic University

学位级别：Ph.D., Doctor of Philosophy

Salt marshes are highly dynamic ecosystems that rely on multiple environmental and physical drivers that determine species distribution and soil property distribution. However, climate change and human interference are threatening the delicate ecosystem. One of the easiest ways to monitor marsh dynamics is through remote sensing. Traditional methods may not handle the large, non-parametric datasets well and often do not spatially determine areas of uncertainty. This dissertation research developed a framework to map marsh species and predict ground soil properties using multiple remote sensing data sources by integrating modern object-based image analysis (OBIA), machine learning, data fusion, and band indices techniques. It also sought to determine areas of uncertainty in the final outputs and differences between different spectral resolutions. Five machine learning classifiers were examined including Support Vector Machine (SVM) and Random Forest (RF) to map marsh species. Overall results illustrated that RF and SVM typically performed best, especially when using hyperspectral data combined with DEM information. Seven regressors were assessed to map three different soil properties. Again, RF and SVM performed the best no matter the dataset used, or soil property mapped. Soil salinity had r as high as 0.93, soil moisture had r as high as 0.91, and soil organic an r as high as 0.74 when using hyperspectral data. The multispectral data had r as high as 0.88 for salinity and 0.81 for moisture, increasing accuracy over previous attempts in similar studies. Soil organic predictions using multispectral data still need more work. An uncertainty analysis on both the species classifications and the soil property predictions suggested that most of the areas of disagreement occurred along the tidal creeks and upper elevations of the marsh. Finally, a joint analysis was conducted by relating marsh species map to soil property map to determine where each species was predominate

关键词： Machine learning Marsh dynamics object-based image analysis Soil property mapping Species mapping

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Application and Evaluation of the AI-Powered Segment Anything Model (SAM) in Seafloor Mapping: A Case Study from Puck Lagoon, Poland

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REMOTE SENSING 2024年第14期16卷 2638页

作者： Janowski, Lukasz Wroblewski, Radoslaw Gdynia Maritime Univ Maritime Inst Roberta Plelo 20 PL-80548 Gdansk Poland Univ Gdansk Dept Geophys Pilsudskiego 46 PL-81378 Gdynia Poland MEWO SA Starogardzka 17A PL-83010 Straszyn Poland

The digital representation of seafloor, a challenge in UNESCO's Ocean Decade initiative, is essential for sustainable development support and marine environment protection, aligning with the United Nations' 2030 program goals. Accuracy in seafloor representation can be achieved through remote sensing measurements, including acoustic and laser sources. Ground truth information integration facilitates comprehensive seafloor assessment. The current seafloor mapping paradigm benefits from the object-based image analysis (OBIA) approach, managing high-resolution remote sensing measurements effectively. A critical OBIA step is the segmentation process, with various algorithms available. Recent artificial intelligence advancements have led to AI-powered segmentation algorithms development, like the Segment Anything Model (SAM) by META AI. This paper presents the SAM approach's first evaluation for seafloor mapping. The benchmark remote sensing dataset refers to Puck Lagoon, Poland and includes measurements from various sources, primarily multibeam echosounders, bathymetric lidar, airborne photogrammetry, and satellite imagery. The SAM algorithm's performance was evaluated on an affordable workstation equipped with an NVIDIA GPU, enabling CUDA architecture utilization. The growing popularity and demand for AI-based services predict their widespread application in future underwater remote sensing studies, regardless of the measurement technology used (acoustic, laser, or imagery). Applying SAM in Puck Lagoon seafloor mapping may benefit other seafloor mapping studies intending to employ AI technology.

关键词： Segment Anything object-based image analysis underwater remote sensing multibeam echosounder bathymetric LiDAR satellite-derived bathymetry seafloor mapping geomorphological bedforms Puck Lagoon

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STATE-WIDE WETLAND INVENTORY MAP OF MINNESOTA USING MULTI-SOURCE AND MULTI-TEMPORALREMOTE SENSING DATA 24

STATE-WIDE WETLAND INVENTORY MAP OF MINNESOTA USING MULTI-SO...

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24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow

作者： Igwe, Victor Salehi, Bahram Mandianpari, Masoud SUNY Coll Environm Sci & Forestry SUNY ESF Dept Environm Resources Engn Syracuse NY 13210 USA Mem Univ Newfoundland C CORE St John NL A1B 3X5 Canada Mem Univ Newfoundland Dept Elect & Comp Engn St John NL A1B 3X5 Canada

Carbon sequestration coupled with flood mitigation and other functions of wetlands, such as water filtration, coastal protection, biodiversity, and providing recreational spots, make wetland mapping and monitoring important for different countries. Google Earth Engine (GEE) cloud computing platform is becoming a very important tool for lots of environmental studies as it provides a suite of tools and access to data that facilitate large-scale environmental monitoring projects through its powerful parallel processing capabilities. In this study, we use GEE to access multi-source remote sensing datasets and implement an object-based image analysis, and random forest algorithm for the classification of wetlands in the state of Minnesota. Emergent, forested, and scrub-shrub wetland classes, water, as well as urban, forest, and agriculture land cover types were classified using Sentinel-2, Sentinel-1, USGS 3D Elevation Program 10-meter DEM, and gridded soil data. NDVI, EVI, BSI, NDBI, and NDWI spectral indices were calculated from Sentinel-2 imagery, VV and VH polarization channels, and their ratio, as well as span parameters, were calculated from Sentinel-1 imagery, and slope and aspect features were extracted from DEM. Simple Non-Iterative Clustering (SNIC), Gray-Level Co-occurrence Matrix (GLCM), Principal Components analysis (PCA), and random forest algorithms were implemented to classify wetlands from the GEE platform. Emergent wetlands, water, urban, and agriculture classes performed well with producer accuracies greater than 90%. Sentinel-1, DEM, and soil datasets improve the identification of wetland classes and highlight the importance of multi-source approaches for wetland mapping.

关键词： Wetlands remote sensing multi-source data google earth engine Minnesota random forest object-based image analysis

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object-based image analysis of WORLDVIEW-2 SATELLITE DATA for the CLASSIFICATION of MANGROVE AREAS in the CITY of SÃO LUÍS, MARANHÃO STATE, BRAZIL 22

OBJECT-BASED IMAGE ANALYSIS of WORLDVIEW-2 SATELLITE DATA fo...

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22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012

作者： Kux, H.J.H. Souza, U.D.V. INPE Andndash National Institute for Space Research Avenida Dos Astronautas 1758 andndashJardim da Granja São José dos Campos andndashSP12.227-010 Brazil

Taking into account the importance of mangrove environments for the biodiversity of coastal areas, the objective of this paper is to classify the different types of irregular human occupation on the areas of mangrove vegetation in São Luis, capital of Maranhão State, Brazil, considering the OBIA (object-based image analysis) approach with WorldView-2 satellite data and using Interimage, a free image analysis software. A methodology for the study of the area covered by mangroves at the northern portion of the city was proposed to identify the main targets of this area, such as: marsh areas (known locally as Apicum), mangrove forests, tidal channels, blockhouses (irregular constructions), embankments, paved streets and different condominiums. Initially a databank including information on the main types of occupation and environments was established for the area under study. An image fusion (multispectral bands with panchromatic band) was done, to improve the information content of WorldView-2 data. Following an ortho-rectification was made with the dataset used, in order to compare with cartographical data from the municipality, using Ground Control Points (GCPs) collected during field survey. Using the data mining software GEODMA, a series of attributes which characterize the targets of interest was established. Afterwards the classes were structured, a knowledge model was created and the classification performed. The OBIA approach eased mapping of such sensitive areas, showing the irregular occupations and embankments of mangrove forests, reducing its area and damaging the marine biodiversity.

关键词： High-resolution images Highresolution images Irregular occupation Mangrove object-based image analysis Urban area. WorldView-2

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The potential of Gokturk 2 satellite images for mapping burnt forest areas

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TURKISH JOURNAL OF AGRICULTURE AND FORESTRY 2021年第1期45卷 91-101页

作者： Kuruca, Murat Matci, Dilek Kocuk Avdan, Ugur Eskisehir Tech Univ Res Inst Earth & Space Sci Eskisehir Turkey

Using remotely sensed data to identify burnt forest areas produces fast, economical, and highly accurate results. Accordingly, in this study we investigate the capabilities of Gokturk-2, Turkey's national satellite, for mapping burnt forest areas. We compare our results with those obtained from Landsat-8 and Worldview-2 satellite images, which are frequently used for mapping burnt areas. The capabilities of the satellites are compared, in terms of detecting burnt forest areas, using support vector machine (SVM) and rotation forest (RF) classification, which are advanced methods.-According to the results of the accuracy analysis, SVM classification gives similar kappa statistics and overall accuracy for Gokturk-2 and Landsat-8 images, while the performance of Worldview-2 shows greater general accuracy. Although there is no significant difference between the two classification methods forLandsat-8 images, SVM gives better results than RF for both Gokturk-2 and Worldview-2. The results of our study show that Gokturk-2 images are an effective source for mapping burnt forest areas.

关键词： Gokturk-2 mapping burnt forest area object-based image analysis rotation forest support vector machine

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Comparing Pixel- and object-based Approaches for Classifying Multispectral Drone imagery of a Salt Marsh Restoration and Reference Site

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REMOTE SENSING 2024年第6期16卷 1049页

作者： Norris, Gregory S. LaRocque, Armand Leblon, Brigitte Barbeau, Myriam A. Hanson, Alan R. Univ New Brunswick Fac Forestry & Environm Management Fredericton NB E3B 5A3 Canada Lakehead Univ Fac Nat Resource Management Thunder Bay ON P7B 5E1 Canada Univ New Brunswick Dept Biol Fredericton NB E3B 5A3 Canada Canadian Wildlife Serv Environm Canada POB 6227 Sackville NB E4L 4N1 Canada

Monitoring salt marshes with remote sensing is necessary to evaluate their state and restoration. Determining appropriate techniques for this can be overwhelming. Our study provides insight into whether a pixel- or object-based Random Forest classification approach is best for mapping vegetation in north temperate salt marshes. We used input variables from drone images (raw reflectances, vegetation indices, and textural features) acquired in June, July, and August 2021 of a salt marsh restoration and reference site in Aulac, New Brunswick, Canada. We also investigated the importance of input variables and whether using landcover classes representing areas of change was a practical way to evaluate variation in the monthly images. Our results indicated that (1) the classifiers achieved overall validation accuracies of 91.1-95.2%;(2) pixel-based classifiers outperformed object-based classifiers by 1.3-2.0%;(3) input variables extracted from the August images were more important than those extracted from the June and July images;(4) certain raw reflectances, vegetation indices, and textural features were among the most important variables;and (5) classes that changed temporally were mapped with user's and producer's validation accuracies of 86.7-100.0%. Knowledge gained during this study will inform assessments of salt marsh restoration trajectories spanning multiple years.

关键词： image classification ecological restoration wetland pixel-based image analysis object-based image analysis Random Forest Bay of Fundy

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