Digital Aerial Images Research Articles

Use of Drones for Trough Reading, Animal Counting, and Production Monitoring in Feedlot Systems

In line with the concept of precision agriculture, this study aimed to validate the use of digital aerial images captured using a remotely piloted aircraft (RPA) for collecting zootechnical data on cattle feedlot systems in a tropical environment. Images were captured on 21 non-consecutive days in 110 pens with up to 150 animals each. Conventional and RPA-based methods were adopted to determine animal behavior, feed trough levels, animal counts, and pen conditions. Data analysis revealed almost perfect agreement (kappa coefficient = 0.901) between trough readings taken by conventional and RPA methods as well as substantial agreement for fecal score (kappa coefficient = 0.785) and surface conditions (kappa coefficient = 0.737). However, animal counts and water quality scores showed only fair agreement, suggesting challenges in using RPA for these specific tasks. The results indicated that RPA represents a viable alternative to conventional methods for monitoring zootechnical indices in feedlots, offering benefits in terms of accuracy, efficiency, and cost-effectiveness. The implementation of RPA-based methods holds potential for improving animal management, welfare, and yield in feedlot systems.

Transformer-based multimodal change detection with multitask consistency constraints

Change detection plays a fundamental role in Earth observation for analyzing temporal iterations over time. However, recent studies have largely neglected the utilization of multimodal data that presents significant practical and technical advantages compared to single-modal approaches. This research focuses on leveraging pre-event digital surface model (DSM) data and post-event digital aerial images captured at different times for detecting change beyond 2D. We observe that the current change detection methods struggle with the multitask conflicts between semantic and height change detection tasks. To address this challenge, we propose an efficient Transformer-based network that learns shared representation between cross-dimensional inputs through cross-attention. It adopts a consistency constraint to establish the multimodal relationship. Initially, pseudo-changes are derived by employing height change thresholding. Subsequently, the L2 distance between semantic and pseudo-changes within their overlapping regions is minimized. This explicitly endows the height change detection (regression task) and semantic change detection (classification task) with representation consistency. A DSM-to-image multimodal dataset encompassing three cities in the Netherlands was constructed. It lays a new foundation for beyond-2D change detection from cross-dimensional inputs. Compared to five state-of-the-art change detection methods, our model demonstrates consistent multitask superiority in terms of semantic and height change detection. Furthermore, the consistency strategy can be seamlessly adapted to the other methods, yielding promising improvements.

Improving urban tree species classification by deep-learning based fusion of digital aerial images and LiDAR

Accurate information on tree species distribution in urban areas can offer insights into how street trees provide ecosystem services, such as air pollution mitigation and surface cooling. This article presents a method to improve tree species classification in a tropical urban area using LiDAR-derived structural properties of individual tree crowns (ITCs) and digital aerial images. We extracted four LiDAR features, including surface normals of tree leaves, intensity, tree height, and leaf area index (LAI). We conducted two experiments: In the first, we trained encoder-decoder convolutional neural networks using a stack of optical bands and one LiDAR feature at a time. In the second, we developed an optical-LiDAR fusion strategy that combined feature maps from two encoder-decoder networks. One network was trained with optical bands only, while the other was trained with the LiDAR features that improved classification accuracy in the first experiment. Our experiment results demonstrated the usefulness of surface normals and intensity in discriminating among tree species. We found that the optical-LiDAR fusion strategy increased the average F1-score by 12.6 percentage points compared to only optical bands. We also employed the new segment anything (SAM) model to automatically delineate ITCs. SAM outlined ITCs with a boundary F1-score of 98%. The SAM-delineated ITCs were used to improve raw model predictions and produce reliable species maps. This study contributes to mapping and monitoring urban tree species in tropical areas.

Aerial photogrammetry of seabirds from digital aerial video images using relative change in size to estimate flight height

Aerial photogrammetry of seabirds from digital aerial video images using relative change in size to estimate flight height

Orthofaçade-Based Assisted Inspection Method for Buildings

Building façade assessment could be performed in a more efficient way using a multidisciplinary approach and modern technologies. This study proposes the orthofaçade-based assisted inspection method (AIM), universal and applicable to different types of façade cladding and suitable for application in the condition assessment of inaccessible building façades or high-rise and large structures of all kinds. The AIM method offers a multidisciplinary approach by combining unmanned aerial vehicle (UAV) technology, electronic tachymetry, and digital image processing techniques (photogrammetry and open-source computer vision methods). The method was verified in a case study performed on a high-rise building façade. On-site data acquisition of high-resolution images of façade and control points was conducted by UAV and tachymetry. The data were further processed in photogrammetric software in order to generate a georeferenced orthofaçade. Crack detection was performed at pixel level via computer code using the OpenCV library methods. The established diagnostic model, defined by control points, enables precise determination of crack location. Crack length, width, or area could be calculated based on the coordinates of its points, by performing simple mathematical operations. The AIM method provides automation of crack detection and precise determination of location and geometrical parameters of detected crack.

REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGES

Digital aerial images obtained by cameras embedded in remotely piloted aircraft (RPA) have been used to detect and monitor abiotic stresses in soybeans, such as water and nutritional deficiencies. This study aimed to evaluate the ability of vegetation indexes (VIs) from RPA images to remotely detect water and nutritional status in two soybean cultivars for nitrogen. The soybean cultivars BONUS and BRS-8980 were evaluated at the phenological stages R5 and R3 (beginning of seed enlargement), respectively. To do so, plants were subjected to two water regimes (100% ETc and 50% ETc) and two nitrogen (N) supplementation levels (with and without). Thirty-five VIs from multispectral aerial images were evaluated and correlated with stomatal conductance (gs) and leaf N content (NF) measurements. Near-infrared (NIR) spectral band, enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and renormalized difference vegetation index (RDVI) showed linear correlation (p<0.001) with gs, standing out as promising indexes for detection of soybean water status. In turn, simplified canopy chlorophyll content index (SCCCI), red-edge chlorophyll index (RECI), green ratio vegetation index (GRVI), and chlorophyll vegetation index (CVI) were correlated with NF (p<0.001), thus being considered promising for the detection of leaf N content in soybeans.

Monitoring of Shoreline Change at Chollipo Beach in South Korea

Yun, K.; Lee, C.K.; He, G., and Park, B.W., 2021. Monitoring of shoreline change at Chollipo Beach in South Korea. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 469–473. Coconut Creek (Florida), ISSN 0749-0208. Natural coastal shorelines change constantly due to various phenomena such as tides, waves, and weather. Shoreline change data are important for coastal management, which deals with issues including coastal erosion and sedimentation trends and setbacks. It has been reported that Chollipo Beach in Taeangun, Chungnamdo, in the western part of South Korea, has been progressively sedimented over the past years. In this study, a photogrammetric analysis was conducted to determine the characteristics of the shoreline of Chollipo Beach over twenty years. In the analysis, photogrammetric aerial triangulation using old digital aerial images from 1986 and unmanned aerial vehicle images from 2020 were considered. In addition, network GPS surveying was performed for the acquisition of ground control points in the bundle adjustments stage. Finally, a digital surface model and mosaicked orthoimages were generated. For the analysis, contour lines and beach profiles were extracted. The analysis results show that over 25 years, the shorelines moved towards the sea by a maximum distance of 100 m and deposited sands reached a height of 1.84 m.

The influence of window size on remote sensing-based prediction of forest structural variables

BackgroundDetermining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data. Because the value of the reference laser and image metrics that affect the quality of the prediction model depends on window size. However, suitable window sizes are usually determined by trial and error. There are a limited number of published studies evaluating appropriate window sizes for different remote sensing data. This research investigated the effect of window size on predicting forest structural variables using airborne LiDAR data, digital aerial image and WorldView-3 satellite image.ResultsIn the WorldView-3 and digital aerial image, significant differences were observed in the prediction accuracies of the structural variables according to different window sizes. For the estimation based on WorldView-3 in black pine stands, the optimal window sizes for stem number (N), volume (V), basal area (BA) and mean height (H) were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. In oak stands, the R2 values of each moving window size were almost identical for N and BA. The optimal window size was 400 m2 for V and 600 m2 for H. For the estimation based on aerial image in black pine stands, the 800 m2 window size was optimal for N and H, the 600 m2 window size was optimal for V and the 1000 m2 window size was optimal for BA. In the oak stands, the optimal window sizes for N, V, BA and H were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. The optimal window sizes may need to be scaled up or down to match the stand canopy components. In the LiDAR data, the R2 values of each window size were almost identical for all variables of the black pine and the oak stands.ConclusionThis study illustrated that the window size has an effect on the prediction accuracy in estimating forest structural variables based on remote sensing data. Moreover, the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.

Analyzing glacier retreat and mass balances using aerial and UAV photogrammetry in the Ötztal Alps, Austria

Abstract. We use high-resolution aerial photogrammetry to investigate glacier retreat in great spatial and temporal detail in the Ötztal Alps, a heavily glacierized area in Austria. Long-term in situ glaciological observations are available for this region as well as a multitemporal time series of digital aerial images with a spatial resolution of 0.2 m acquired over a period of 9 years. Digital surface models (DSMs) are generated for the years 2009, 2015, and 2018. Using these, glacier retreat, extent, and surface elevation changes of all 23 glaciers in the region, including the Vernagtferner, are analyzed. Due to different acquisition dates of the large-scale photogrammetric surveys and the glaciological data, a correction is successfully applied using a designated unmanned aerial vehicle (UAV) survey across a major part of the Vernagtferner. The correction allows a comparison of the mass balances from geodetic and glaciological techniques – both quantitatively and spatially. The results show a clear increase in glacier mass loss for all glaciers in the region, including the Vernagtferner, over the last decade. Local deviations and processes, such as the influence of debris cover, crevasses, and ice dynamics on the mass balance of the Vernagtferner, are quantified. Since those local processes are not captured with the glaciological method, they underline the benefits of complementary geodetic surveying. The availability of high-resolution multi-temporal digital aerial imagery for most of the glaciers in the Alps provides opportunities for a more comprehensive and detailed analysis of climate-change-induced glacier retreat and mass loss.

Multi-temporal analysis of erosional plots using aerial images and deep soil probes

ABSTRACT Evaluating erosion processes and their influence on soil degradation over extensive agricultural landscape is difficult. Because of the need for analysis and mapping of this problem, we elaborated a procedure applicable to a specific, intensively farmed, erosion-threatened chernozem area of CZ or similar regions. The aim of the research was to verify the possibility of digital aerial image analysis, along with a control study of soil sampling. To define and compare the boundaries and extent of erosion and deposition processes, we used images from two different years, showing changes in soil degradation. Orthorectification and unsupervised image classification were followed by interpretation and statistical evaluation. The results, locating erosional, transitional and depositional areas, were verified in laboratory analysis of soil samples for selected soil characteristics. Comparison of aerial images showed changes in the agricultural landscape over 35 years, showing an increase of ca. 60% in erosional areas. Extensive changes also occurred in transitional and depositional areas as material from the subsoil horizon of erosional areas covered some depositional areas. Importantly, our research confirmed the potential of digital aerial image analysis as a method of studying soil erosion over extensive landscape, and the possibility of using it in planning anti-erosion measures.

NITROGEN ESTIMATION IN SUGARCANE FIELDS FROM AERIAL DIGITAL IMAGES USING ARTIFICIAL NEURAL NETWORK

Knowing the nitrogen status of crop is essential for agricultural operations management, nevertheless conventional methods are time consuming and expensive. In this research, possibility of using digital aerial images as a remote sensing method to determine the nitrogen content of sugarcane plant was studied. Arial images were captured from 3 sugarcane fields using a 12.9-megapixel digital camera mounted on a Phantom 3 quad-copter from 5 and 10 m heights. At the same time, four healthy top branches of sugarcane plants were cut from imaging points as plant samples. The nitrogen value of the samples was measured using Kjeldahl test at laboratory. Multilayer perceptron (MLP) artificial neural network (ANN) algorithm was used to estimate nitrogen status in the crop from the aerial digital images. Color indices of images were extracted using image processing in MATLAB software and their correlation with the nitrogen value were determined. The indices that had correlation with nitrogen were selected as inputs of the ANNs and the nitrogen value was the output. There was no significant difference between the nitrogen values predicted by ANNs and its actual values. The average errors of the ANNs training were 0.145 and 0.022 and the correlation coefficients of the predicted and actual values of nitrogen were 0.89 and 0.94, for 5 m and 10 m heights respectively. Also, the RMSE values of nitrogen estimation was 0.181 and 0.174, at 5 m and 10 m heights respectively. So, nitrogen estimation of sugarcane fields is possible by aerial digital imaging.

Urban Feature Extraction from Merged Airborne LiDAR Data and Digital Camera Data

Until recently, the most highly accurate digital surface models were obtained from airborne lidar. With the development of a new generation of large format digital photogrammetric aerial camera, a fully digital photogrammetric workflow became possible. Digital airborne images are sources for elevation extraction and orthophoto generation. This research concerned with the generation of digital surface models and orthophotos as applications from high-resolution images. In this research, the following steps were performed. A Benchmark data of LIDAR and digital aerial camera have been used. Firstly, image orientation, AT have been performed. Then the automatic digital surface model DSM generation has been produced from the digital aerial camera. Thirdly true digital ortho has been generated from the digital aerial camera also orthoimage will be generated using LIDAR digital elevation model (DSM). Leica Photogrammetric Suite (LPS) module of Erdsa Imagine 2014 software was utilized for processing. Then the resulted orthoimages from both techniques were mosaicked. The results show that automatic digital surface model DSM that been produced from digital aerial camera method has very high dense photogrammetric 3D point clouds compared to the LIDAR 3D point clouds. It was found that the true orthoimage produced from the second approach is better than the true orthoimage produced from the first approach. The five approaches were tested for classification of the best-orthorectified image mosaic using subpixel based (neural network) and pixel-based ( minimum distance and maximum likelihood).Multicues were extracted such as texture(entropy-mean),Digital elevation model, Digital surface model ,normalized digital surface model (nDSM) and intensity image. The contributions of the individual cues used in the classification have been evaluated. It was found that the best cue integration is intensity (pan) +nDSM+ entropy followed by intensity (pan) +nDSM+mean then intensity image +mean+ entropy after that DSM )image and two texture measures (mean and entropy) followed by the colour image. The integration with height data increases the accuracy. Also, it was found that the integration with entropy texture increases the accuracy. Resulted in fifteen cases of classification it was found that maximum likelihood classifier is the best followed by minimum distance then neural network classifier. We attribute this to the fine resolution of the digital camera image. Subpixel classifier (neural network) is not suitable for classifying aerial digital camera images.

Long-Term Changes of Morphodynamics on Little Ice Age Lateral Moraines and the Resulting Sediment Transfer into Mountain Streams in the Upper Kauner Valley, Austria

Since the end of the Little Ice Age (LIA), formerly glaciated areas have undergone considerable changes in their morphodynamics due to external forces and system-internal dynamics. Using multi-temporal high-resolution digital elevation models (DEMs) from different remote sensing techniques such as historical digital aerial images and light detection and ranging (LiDAR), and the resulting DEMs of difference (DoD), spatial erosion and accumulation patterns can be analyzed in proglacial areas over several decades. In this study, several morphological sediment budgets of different test sites on lateral moraines and different long-term periods were determined, covering a total period of 49 years. The test sites show high ongoing morphodynamics, and therefore low vegetation development. A decrease as well as an increase of the mean annual erosion volume could be demonstrated at the different test sites. All test sites show a slope–channel coupling and a decrease in the efficiency of sediment transport from slopes to channels. These developments are generally subject to conditions of increasing temperature, decreasing short-term precipitation patterns and increasing runoff from adjacent mountain streams. Finally, the study shows that sediment is still available on the investigated test sites and the paraglacial adjustment process is still in progress even after several decades of deglaciation (~133 years).

Canopy greenness as a midseason nitrogen management tool in corn production

AbstractAn in‐season nitrogen (N) assessment tool would be valuable in improving N management in corn (Zea mays L.). This study evaluated the intensity of canopy greenness, as determined by the Dark Green Color Index (DGCI) measured from aerial digital images, to serve as a guideline for in‐season N management. Measurements of DGCI from high N (258 kg N ha−1) experimental units were used as a reference DGCI (RDGCI) to correct for variable lighting conditions. Ability of DGCI to assess in‐season N status was investigated by predicting leaf N (LN) concentration and relative grain yield (RGY) as functions of DGCI and RDGCI. Eight field studies were conducted on silt loam soils in Arkansas. Digital images and leaf samples were collected at the 10th collared leaf stage (V10), the 12th collared leaf stage (V12), and tasseling (VT). Across a wide range of environmental lighting conditions, LN was described by a regression (P &lt; .0001) as a function of DGCI and RDGCI with a root mean square error of 3.49 g N kg−1 and a bias of −0.03 g N kg−1. Likewise, RGY was regressed against DGCI and RDGCI (P &lt; .0001) and predicted RGY with a RMSE of 10.3% and a bias of −0.3%. Measurements of DGCI and RDGCI predicted LN and RGY accurately, demonstrating the potential to assess N status and improve N management in corn. These relationships provide reliable metrics for N status during late vegetative and early reproductive growth using aerial images.

Estimation of winter-wheat above-ground biomass using the wavelet analysis of unmanned aerial vehicle-based digital images and hyperspectral crop canopy images

ABSTRACT The crop above-ground biomass (AGB) is critically important for monitoring crop growth, and its accurate estimation can be used by agricultural managers to improve farmland management and to predict crop grain yield. Many studies have shown that models for the estimation of AGB for multiple crop growth stages based on optical remote sensing spectral indices (SIs) often underestimate the crop AGB in later growth stages due to saturation problems. The purpose of this study was to estimate winter-wheat AGB using (i) high-frequency information obtained from the image wavelet decomposition (IWD) of unmanned aerial vehicle (UAV)-based digital images of winter-wheat canopy (i.e. high-frequency IWD information), and (ii) variables obtained from the continuous wavelet transform (CWT) of hyperspectral images of winter-wheat canopy (i.e. hyperspectral CWT variables). Digital and hyperspectral images were acquired using a digital camera and an imaging spectrometer, both mounted on a UAV. Unlike optical SIs, high-frequency IWD information and hyperspectral CWT variables may not be limited by saturation problems for high winter-wheat canopy cover. Our results indicate that high-frequency IWD information and hyperspectral CWT variables both increase (decrease) with increasing winter-wheat AGB. A multiple linear stepwise regression technique was used to analyse the performance of (1) SIs, (2) CWT, (3) IWD, (4) SIs + CWT, (5) SIs + IWD, (6) CWT + IWD, and (7) SIs + CWT + IWD, for estimating AGB. Our results indicate that: (i) the method based only on optical SIs may not support the estimation of winter-wheat AGB for multiple growth stages (SIs: coefficient of determination (R 2) = 0.62, mean absolute error (MAE) = 1.30 t ha–1, root-mean-square error (RMSE) = 1.63 t ha–1); (ii) the combined use of the IWD of digital images of winter-wheat canopy and the CWT of hyperspectral images of crop canopy can support the estimation of winter-wheat AGB for multiple growth stages (CWT + IWD: R 2 = 0.85, MAE = 0.79 t ha–1, RMSE = 1.01 t ha–1), including later growth stages; and (iii) the combined use of SIs and the IWD of digital images of winter-wheat canopy can improve the estimation accuracy of winter-wheat AGB (SIs + IWD: R 2 = 0.80, MAE = 0.93 t ha–1, RMSE = 1.22 t ha–1), which may indicate that imaging spectrometers and cheap digital cameras have distinct advantages and can both be used to obtain AGB estimates using different methods. This work provides a new perspective on the use of high-frequency IWD information, hyperspectral CWT variables, and their combination to estimate AGB for multiple crop growth stages.

USE OF HISTORICAL AERIAL IMAGES FOR 3D MODELLING OF GLACIERS IN THE PROVINCE OF TRENTO

Abstract. Historical aerial images represent a source of information of great value for glacier monitoring, as they cover the area of interest at a well-defined epoch and allow for visual interpretation and metric analysis. Typically, the aerial images are used to produce orthophotos and manually digitize the perimeters of the glaciers for analysis of the surface extent of the glaciers, while the extraction of height information is more challenging due to data quality and characteristics. This article discusses the potential of historical aerial images for glacier modelling. More specifically, it analyses the impact of their coverage, radiometric- and geometric accuracy, state of preservation and completeness on the photogrammetric workflow. The data set used consists of scans of 300 (analog) aerial images acquired between August and October 1954 by the U.S. Air Force with a Fairchild KF7660 camera over the entire Province of Trento. For the modelling of the glaciers, different techniques such as manual stereoscopic measurement and dense image matching were tested on sample glaciers and the results were analysed in detail. Due to local radiometric saturation in a large part of the glacial surfaces and other disturbances affecting the historical images (e.g. scratches, scanning errors, dark shadows), dense image matching did not produce any valuable results, and stereo plotting could be used only on images (or image parts) with acceptable quality. The derived Digital Terrain Models (DTMs) were compared with a reference DTM obtained with dense image matching from digital aerial images acquired in September 2015 with an UltraCam Eagle sensor, and, for some glaciers, to a DTM obtained with dense image matching from scanned aerial images acquired in September 1983 with a RC30 analog camera. The differences between 1954 and 2015 DTMs showed values up to 70–80 m in height and a behaviour that is confirmed by the models employed by the glaciology experts in Trento.

TOWARDS THE MODELING OF GLACIER MICROTOPOGRAPHY USING HIGH-RESOLUTION DATA FROM UNMANNED AERIAL SURVEY

Abstract. Glaciated areas are important targets for interdisciplinary research. In the last quarter of the 20th century, there has been a significant shift in glacier observation approaches from direct fieldwork to remote sensing. Over the past 15 years, unmanned aerial systems have been increasingly used for this purpose. In this article, we briefly describe a newly launched Russian–Chinese project aimed at developing a theory and methodology for digital modeling and analysis of the glacier microtopography using very high resolution data from unmanned aerial surveys. We argue the relevance of the study and review key publications on the application of digital terrain modeling and geomorphometry in glaciology. Next, we discuss the aim of the project and tasks performed by the Russian side, as well as materials and methods used in the study. As initial data, we use multi-temporal, digital aerial images of very high resolution (5 cm) collected by the unmanned aerial survey of the ice sheet and glaciers near the Larsemann Hills, East Antarctic. Finally, we present some examples for geomorphometric analysis of glacier microtopography including snow/ice features of eolian origin.

A Novel Privacy Approach of Digital Aerial Images Based on Mersenne Twister Method with DNA Genetic Encoding and Chaos

Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes.

A process for the accurate reconstruction of pre-filtered and compressed digital aerial images

The study of compression and decompression methods is crucial for storage and/or transmission of large numbers of image data which is required for archiving aerial photographs, satellite images and digital ortho-photos. Hence, the proposed work aims to increment the compression ratio (CR) of digital images in general. While emphasis is made on aerial images, the same principle may find applications to other types of raster based images. The process described here involves the application of pre-defined low-pass filters (i.e. kernels) prior to applying standard image compression encoders. Low-pass filters have the effect of increasing the dependence between neighbouring pixels which can be used to improve the CR. However, for this pre-filtering process to be considered as a compression instrument, it should allow for the original image to be accurately restored from its filtered counterpart.The development of the restoration process presented in this study is based on the theory of least squares and assumes the knowledge of the filtered image and the low-pass filter applied to the original image. The process is a variant of a super-resolution algorithm previously described, but its application and adaptation to the filtering and restoration of images, in this case (but not exclusively) aerial imagery, using a number of scales and filter dimensions is the expansion detailed here. An example of the proposed process is detailed in the ensuing sections. The example is also indicative of the degree of accuracy that can be attained upon applying this process to gray-scale images of different entropies and coded in a lossy or lossless mode.

Automatic identification of the area covered by acorn trees in the dehesa (pastureland) Extremadura of Spain

The acorn is the fruit of oak trees and is an important crop in the Spanish dehesa extremeña because of the value it provides to feeding Iberian pigs to obtain the “acorn-fed” certification. For this reason, it is desirable to maximise production of Iberian pigs with the appropriate weight. Knowing the area covered by the acorn treetops to determine the Tree-Covered Area (TCA), a value required to estimate the number of Iberian pigs that can be released per hectare, as regulated by the Royal Decree 4/2014, issued by Spain in January of 2014. In this work, we propose a method to automatically estimate the TCA through digital aerial images (orthophotos) of the pastureland of Extremadura and, with this, to facilitate determining the number of Iberian pigs that can be released in a specific parcel of land. Requirements for automatic detection include the correct identification of acorn trees, correctly discerning their shadows, and discerning arbuscles (young acorn trees that are still unproductive, or shrubs that are not oaks). These requirements represent challenges that apply to both automatic and manual segmentations. The automated segmentation in this study is based on the modified version of the Gustafson-Kessel clustering algorithm presented by Babuska. The results of this work are promising when compared with the actual images and when compared with the results of manual segmentation. The entire set of 38 aerial images used in this work correspond to an approximate area of 15.56 hectares, and the results are of great interest to producers of Iberian pork with the “acorn-fed” certification.