Image Processing and Analysis Methods

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Spectral mixture analysis (SMA) is an image-processing technique used for the analysis of airborne hyperspectral remote-sensing data which consist of a large number of spectral bands, typically over 100. In this paper the possibilities are examined of using SMA for the analysis of Landsat Thematic Mapper satellite data with only six bands in the visible to shortwave-infrared wavelength. We use data from a semi-arid area in the Sanmatenga province of Burkina Faso, an area known to experience land-degradation problems. In SMA, we assume that pixels in an image consist of one or more homogeneous (uniform in character) or pure surfaces, the so called end-members. The end-members were derived from the image data on the basis of specific image characteristics. Field data was collected to describe the characteristics of these end-members in terms of land cover, soil and degradation phenomena. The end-members derived from the image analysis, although statistically pure in terms of image spectral characteristics, prove to be mixtures at a field scale. This lack of purity is explained by the nature of semi-arid areas which is more heterogeneous than that of most other areas. The SMA yielded cover percentages for the end-members from which an unsupervised classification was made. Comparison of the classification with the results of SMA shows that the latter provides information on the amount and spatial distribution of land characteristics such as land degradation.

Modelling species distributions with remote sensing data: bridging disciplinary perspectives

Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop variability (e.g., nutrient status and pest damage). UAS sensors with high spectral resolution used to compute informative vegetation indices, however, are practically limited by high cost and data dimensionality. This research extends spectral analysis for remote crop monitoring to investigate the relationship between crop health and 3D canopy structure using low-cost UAS equipped with consumer-grade RGB cameras. We used flue-cured tobacco as a case study due to its known sensitivity to fertility variation and nutrient-specific symptomology. Fertilizer treatments were applied to induce plant health variability in a 0.5 ha field of flue-cured tobacco. Multi-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM). Plant structural metrics were then computed from detailed high resolution canopy surface models (0.05 m resolution) interpolated from the photogrammetric point clouds. The UAS surveys were complimented by nutrient status measurements obtained from plant tissues. The relationships between foliar nitrogen (N), phosphorus (P), potassium (K), and boron (B) concentrations and the UAS-derived metrics were assessed using multiple linear regression. Symptoms of N and K deficiencies were well captured and differentiated by the structural metrics. The strongest relationship observed was between canopy shape and N foliar concentration (adj. r2 = 0.59, increasing to adj. r2 = 0.81 when combined with the spectral index). B foliar concentration was consistently better predicted by canopy structure with a maximum adj. r2 = 0.41 observed at the latest growth stage surveyed. Overall, combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results suggest that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low cost UAS for precision agriculture.

Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification

Up-to-date information about vegetation types and hydromorphological structures and features are essential for the management of waterways. They are e.g. used for the monitoring and reporting of riparian statuses and their changes e.g. after river restoration and consequently, numerous man-days are spent on field surveys. To allow for an effective survey of vegetation and hydromorphology in large or even inaccessible areas, a data acquisition and processing workflow is being developed complementing in-situ methods with remote sensing techniques. This is part of the joint research project “mDRONES4rivers” funded by the German Federal Ministry of Transport and Digital Infrastructure (19F2054A). Aerial surveys by unmanned aerial systems (UAS) and a gyrocopter are combined with ground measurements of hyperspectral reflectance signatures as well as with field mapping of vegetation types and hydromorphological structures and features. The remote sensing data is classified with an object based image analysis and classification algorithm. The mobile and (at selected sites) permanent measurements of hyperspectral field data and the typical field surveys provide data for calibration. Contrary to other approaches that focus on what can be detected and classified with certain sensor systems and datasets, the project addresses equally the user needs to obtain certain classes for monitoring and reporting. The intended results are (i) data acquisition, correction and classification workflow combining remote sensing and field data, identification and change detection (ii) of important vegetation and biotope types and (iii) of hydromorphological structures and substrate as well as indicators necessary for the evaluation of the hydromorphological quality. The preliminary results to be presented include datasets from UAS, gyrocopter, and field surveys, an outline of processing workflow and classification algorithm based on Python scripts and eCognition software and first vegetation and hydromorphological classification results from spring and summer datasets. In conclusion, procedures and algorithms are developed to use remote sensing in combination with and for the reduction of time-consuming traditional field surveys as a future operational tool for monitoring riparian vegetation and structures.

Digital image processing, post-processing, and data integration techniques as applied to airborne and satellite remotely sensed data for the purpose of extracting useful Earth resources information will be discussed in this chapter. Image preprocessing and data reduction tools are described in the previous chapter. The concepts discussed in this chapter include: • Image processing techniques such as unsupervised image classifications, supervised image classifications, neural network classifiers, simulated annealing classifiers, and fuzzy logic classification systems • The most widely accepted indices and land use/land cover classification schemes • Post-processing techniques such as filtering and change detection • Accuracy assessment and validation of results • Data integration and spatial modeling including examples of integration of remotely sensed data with other conventional survey and map form data for Earth observation purposes

Aims: This study aimed to evaluate the quality and accuracy of benthic habitat classification results based on drone imagery, verified through field data (ground truthing) based on drone flight time and sensor angle. Study Design: A stratified random sampling method was employed to collect benthic habitat data across six substrate-based strata, yielding 189 observation points. Of these, 95 were used for training and 94 for validation during classification and accuracy assessment. Place and Duration of Study: The study was conducted at Dulanga Beach, Gorontalo Regency, Indonesia. Data collection occurred from June to July 2025, with UAV imagery acquired on 14–15 June 2025. Methodology: UAV data were acquired using an Autel Evo II Pro V3 drone at 100 m altitude with 80% front-lap and side-lap, across four time intervals (08:00, 10:00, 12:00, and 14:00 Ante Meridiem (AM) local time) and two camera angle configurations (90° nadir and 45° oblique). Image pre-processing involved orthophoto generation via Agisoft Metashape using Structure from Motion (SfM), followed by mosaicking and orthorectification. Image analysis applied a two-level object-based image analysis (OBIA) workflow. Level-1 segmentation (scale 200) distinguished broad habitat zones, while Level-2 segmentation (scale 50) delineated benthic classes. Classification combined contextual rule-based editing and Support Vector Machine (SVM) algorithms implemented in eCognition Developer 8.7. Accuracy assessment was conducted using a confusion matrix to calculate overall, producer, and user accuracy, along with the Kappa coefficient. Results: The optimal acquisition was achieved at 10:00 AM local time with a 90° camera angle. Under these conditions, SVM classification with a segmentation scale of 50 yielded an overall accuracy of 91.75% and a Kappa coefficient of 0.89, indicating almost perfect agreement with field validation. Conclusion: UAV-based object-oriented classification offers an accurate and efficient approach for benthic habitat mapping in shallow waters. The findings highlight the significance of acquisition time and camera angle and offer a methodological framework for improving spatial planning, ecosystem monitoring, and the management of marine conservation areas in Gorontalo Province.

ABSTRACTA detailed and up-to-date land use of the urban environment is essentially required in many applications. Very high-resolution (VHR), Multispectral Scanner System (MSS) Worldview-3 (WV-3) satellite imagery provides detailed information on urban characteristics, which should be professionally mined. In this research, WV-3 was processed by machine learning (ML) methods to extract the most accurate urban features. Fuze-Go panchromatic sharpening in conjunction with atmospheric and topographic correction was initially utilized to increase the image quality and colour contrast. Three image analysis approaches including, current pixel-based image analysis (PBIA), object-based image analysis (OBIA) and new feature-based image analysis (FBIA) were implemented on WV-3 image. The k-nearest neighbour (k-NN), Naive Bayes (NB), support vector machine (SVM) classifiers were represented by PBIA, the Decision Tree (DT) classifier was examined as OBIA and the Dempster–Shafer (DS) fusion classifier was manifested for the first time as FBIA. In order to engage DS as FBIA, four types of Belief Masses, namely, Precision, Recall, Overall Accuracy, and kappa coefficient (ĸ) were implemented and compared to assign the most likelihood urban features. All the applied classifiers were also trained on the first site and then tested on another site to examine the transferability. The accuracy, reliability, and computational time of all classifiers were examined by confusion matrix and McNemar assessment. Results show improvements on the detailed urban extraction obtained using the proposed FBIA with 92.2% overall accuracy in compared with PBIA and OBIA. The FBIA result of urban extraction is more consistent when transferred to another study area and consumes much lesser time than OBIA. Also, the precision mass belief measurement achieved highest efficiency regarding receiver operating characteristic (ROC) curve rate.

Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images

Photogrammetric surveying using unmanned aerial systems (UAS) and structure from motion (SfM) is a rapidly emerging technique that has been widely used as a valuable research tool in geodesy and related disciplines. We studied surveying and three-dimensional (3D) modeling based on UAS and SfM. In particular, we focus on the accuracy and quality of UAS-SfM in 3D modeling and volumetric calculation of a building with an overhanging feature. For this purpose, two aerial surveys were performed using a fixed-wing and a multirotor aircraft with nadir and oblique camera axes. For validating the results, field surveys were carried out using terrestrial laser scanner (TLS), differential global navigation satellite system, and total station. To study the performance of SfM software on the accuracy and quality of point clouds, UAS images were processed using three software packages: Agisoft PhotoScan, Pix4Dmapper, and 3Dsurvey. As a result of photogrammetric image processing, 3D dense point clouds, mesh models, and digital surface models were generated. For validating the UAS-SfM results, the models were compared with the TLS model and field measurements; absolute accuracy assessments and model versus model comparisons were performed. The most accurate and high-quality 3D models comparable to TLS models were obtained from oblique images. PhotoScan generated relatively more accurate 3D models with the horizontal and vertical accuracy of root mean square error 0.025 and 0.02 m, respectively. The maximum discrepancies in dimensions and volume measurements from the UAS-SfM-derived point cloud were at the level of 0.01 m and 0.66 m3 (0.03%). The results highlighted that using a low-cost UAS and SfM photogrammetry, high-accuracy and high-quality 3D models can be generated. The results confirm that with respect to its rapid data acquisition and centimeter-level accuracy, UAS-SfM can be considered as a suitable alternative approach to conventional measurement techniques, especially in engineering and emergency response.

Quantifying the rates of geomorphic change is critical for a range of applications within river science and management, including monitoring the evolution of river restoration and assessing the impacts of flow modifications and engineering structures on habitat availability. The tools available for monitoring small river systems at high spatial resolutions are growing with the adoption/availability of airborne/terrestrial laser scanning data and Structure from Motion (SfM)-based
\nphotogrammetry. Monitoring the bathymetry of these smaller stream systems is still limited to traditional survey techniques and these methods do not provide
\nspatially continuous data and can overlook important changes at a range of spatial scales. Recently, we have advanced our capabilities for spatially continuous
\nsurveying of fluvial geomorphology at finer spatial scales using unmanned aerial systems (UAS) and SfM photogrammetry. In this research, we highlight several methodological improvements for the bathymetric SfM process and assess the potential for refraction corrected SfM data to be used for change detection in the lowland
\nRiver Teme in Herefordshire, UK.

Wooded hedgerows do not cover large areas but perform many functions that are beneficial to water quality and biodiversity. A broad range of remotely sensed data is available to map these small linear elements in rural landscapes, but only a few of them have been evaluated for this purpose. In this study, we evaluate and compare various optical remote-sensing data including high and very high spatial resolution, active and passive, and airborne and satellite data to produce quantitative information on the hedgerow network structure and to analyse qualitative information from the maps produced in order to estimate the true value of these maps. We used an object-based image analysis that proved to be efficient for detecting and mapping thin elements in complex landscapes. The analysis was performed at two scales, the hedgerow network scale and the tree canopy scale, on a study site that shows a strong landscape gradient of wooded hedgerow density. The results (1) highlight the key role of spectral resolution on the detection and mapping of wooded elements with remotely sensed data; (2) underline the fact that every satellite image provides relevant information on wooded network structures, even in closed landscape units, whatever the spatial resolution; and (3) indicate that light detection and ranging data offer important insights into future strategies for monitoring hedgerows.

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

The Slumgullion landslide, located in southwestern Colorado, has been active since the early 1700s and current data suggests that the most active portion of the slide creeps at a rate of ~1.5–2.0 cm/day. Accurate deformation measurement techniques are vital to the understanding of persistent, yet slow-moving landslides like the Slumgullion. The factors that affect slope movements at the Slumgullion are on-time scales that are well suited towards a remotely sensed approach to constrain the 12 different kinematic units that make up the persistent creeping landslide. We derive a time series of motion vectors (magnitude and direction) using subpixel offset techniques from very high resolution TerraSAR-X Staring Spotlight ascending/descending data as well as from a novel high-resolution amalgamation of airborne lidar and unmanned aerial systems (UAS) Structure from Motion (SfM) digital surface model (DSM) hillshades. Deformation rates calculated from the spaceborne and airborne datasets show high agreement (mean difference of ~0.9 mm/day), further highlighting the potential for the monitoring of ongoing mass wasting events utilizing unmanned aircraft systems (UAS) We compare pixel offset results from an 11-day synthetic aperture radar (SAR) pair acquired in July of 2016 with motion vectors from a coincident low-cost L1 only Global Navigation Satellite System (GNSS) field campaign in order to verify the remotely sensed results and to derive the accuracy of the azimuth and range offsets. We find that the average azimuth and range pixel offset accuracies utilizing the methods herein are on the order of 1/18 and 1/20 of their along-track and slant range focused ground pixel spacing values of 16.8 cm and 45.5 cm, respectively. We utilize the SAR offset time series to add a twelfth kinematic unit to the previously established set of eleven unique regions at the site of an established minislide within the main landslide itself. Lastly, we compare the calculated rates and direction from all spaceborne- and airborne-derived motion vectors for each of the established kinematic zones within the active portion of the landslide. These comparisons show an overall increased magnitude and across-track component (i.e., more westerly angles of motion) for the descending SAR data as compared to their ascending counterparts. The processing techniques and subsequent results herein provide for an improved knowledge of the Slumgullion landslide’s kinematics and this increased knowledge has implications for the advancement of measurement techniques and the understanding of globally distributed creeping landslides.