On Object-based Image Analysis Research Articles

Enhancing precision in coastal dunes vegetation mapping: ultra-high resolution hierarchical classification at the individual plant level

ABSTRACT The classification of ultra-high-resolution (UHR) imagery, characterized by spatial resolutions exceeding 10 cm, presents opportunities and challenges distinct from lower-resolution counterparts. Particularly, challenges are pronounced in some scenarios, such as mapping plant species in coastal environments, where similar vegetation responses and small plant sizes pose additional difficulties. The present work addressed such issues by developing a UHR vegetation cover classification model at the single plant level using data from uncrewed aerial systems (UASs) equipped with a multispectral optical sensor. The model was tested across the San Rossore Regional Park (Italy), where three pilot areas were defined as training-test-validation sites. The proposed solution consists of a hierarchical two-level-of-detail machine learning model based on object-based image analysis (OBIA) and random forest. This model considers spectral features and indices, elevation, and texture and can classify twelve plant species and two service classes (debris and sand) within the study areas. Train and test were carried out utilizing UAS flight data collected during two specific phenological periods and precise field data derived from in-situ vegetation surveys, which provided 937 herbaceous and shrub samples. The model performance was evaluated based on the error matrix and 50-fold stratified cross-validation method, obtaining an overall accuracy (OA) of 0.76 and a standard deviation of 0.08. Such assessment underscored the crucial role of texture information, in addition to radiometric and elevation. Finally, the model was tested against an unseen dataset, proving its transferability (OA equal to 0.62). Although the discussion highlights some aspects to be further improved and claims for future research, the first version of this hierarchical classification model demonstrated its potential for mapping and monitoring coastal sand dune ecosystems, providing data for understanding and, eventually, modeling ecological and biogeomorphological dynamics.

Object-based crop classification in Hetao plain using random forest

Crop classification based on object-based image analysis (OBIA) is increasingly reported. However, it is still challenging to produce high-quality crop type maps by using recent techniques. This article introduces a new object-based crop classification algorithm which contains 4 steps. First, a random forest (RF) classifier is trained by using the initial training set, which tends to have a relatively small size. Second, importance scores for each feature variable are derived by using the RF model. Third, by treating the importance scores as weighting factors, a weighted Euclidean distance criterion is designed and used for sample creation to enlarge training set. Fourth, RF is re-trained by using the enlarged training set, and then it is employed for final classification. To validate the proposed strategy, a Worldview-2 image covering a part of Hetao plain is experimented. Results indicate that the new method yields the best overall accuracy, which equals 90.52%.

Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment

The increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors, has generated new opportunities and fields for investigation in the engineering geology domain in general and landslide identification and characterization in particular. During the last decade, the so-called Unmanned Aerial Vehicles (UAVs) have been evaluated for diverse applications such as 3D terrain analysis, slope stability, mass movement hazard and risk management. Their advantages of detailed data acquisition at a low cost and effective performance identifies them as leading platforms for site-specific 3D modelling. In this study, the proposed methodology has been developed based on Object-Based Image Analysis (OBIA) and fusion of multivariate data resulted from UAV photogrammetry processing in order to take full advantage of the produced data. Two landslide case studies within the territory of Greece, with different geological and geomorphological characteristics, have been investigated in order to assess the developed landslide detection and characterization algorithm performance in distinct scenarios. The methodology outputs demonstrate the potential for an accurate characterization of individual landslide objects within this natural process based on ultra high-resolution data from close range photogrammetry and OBIA techniques for landslide conceptualization. This proposed study shows that UAV-based landslide modelling on the specific case sites provides a detailed characterization of local scale events in an automated sense with high adaptability on the specific case site.

Spatio-temporal pattern of soil degradation in a Swiss Alpine grassland catchment

Soil degradation on Alpine grasslands is triggered mainly by extreme topography, prevailing climate conditions and land use practices. Suitable monitoring tools are required to assess soil erosion with high temporal and spatial resolution. In this study, we present an unprecedented and comprehensive approach based on object-based image analysis (OBIA) to map and assess all occurring erosion processes within a catchment (Urseren Valley, Switzerland). Five high-resolution (0.25−0.5m) orthophotos with RGB spectral information (SwissImage) produced during a 16-yr period were analyzed. Soil erosion sites are classified according to their type (shallow landslide or sites with reduced vegetation cover affected by sheet erosion) or the triggering land use management impacts (haying, trampling) with the Overall Accuracy ranging between 78 and 88% (Kappa 0.65–0.81) for the different years. The area affected by soil erosion increases for all classes during the study period (2000–2016) by a total of 156 ± 18% (increase consisting of 3% shallow landslides, 5% livestock trails, 46% sheet erosion and 46% management effects). Slopes at lower elevations (<1800masl) are increasingly affected by livestock trails and sheet erosion caused by trampling and grazing as well as other management practices. For areas located above the agricultural land use, an increase in shallow landslides, as well as sheet erosion, can be observed. This points to climate change as a triggering factor of soil degradation, which has not been identified so far as a factor for soil erosion in the Urseren Valley. While OBIA yields conservative estimations mainly due to limitations of spatial resolutions, the method facilitates a comprehensive overview of the ongoing temporal and spatial development regarding soil degradation within the Urseren Valley.

The Classification of Grassland Types Based on Object-Based Image Analysis with Multisource Data

The spatial distribution of different grassland types is important for effectively analyzing spatial patterns, obtaining key vegetation parameters using remote sensing (e.g., biomass, leaf area index, net primary production), and using and protecting grasslands. Existing classifications of grasslands by remote sensing are mostly divided according to the fractional vegetation cover or biomass, but classifications according to grassland types are scarce. In this study, we focused on the classification of different grassland types using remote sensing based on object-based image analysis (OBIA) with multitemporal images in combination with a 30-m digital elevation model (DEM) and the normalized difference vegetation index (NDVI). The grasslands were located in Hulunber, Inner Mongolia, and an autonomous region of China. The support vector machine (SVM) and random forest (RF) machine learning classifiers were selected for the classification. The results revealed the following: 1) It is feasible to generally extract different grassland types on the basis of OBIA with multisource data; the overall classification accuracy and Kappa value exceeded 90% and 0.9, respectively, using the SVM and RF machine learning classifiers, and the classification accuracy of the different grassland types ranged from 61.64% to 98.71%; 2) Multitemporal images and auxiliary data (DEM and NDVI) improved the separability of different grassland types. The information in the growing season was conducive for distinguishing temperate meadow steppe from temperate steppe and was favorable for extracting lowland meadow and swamp in the nongrowing season. The DEM and NDVI also effectively reduced the number of image segmentation objects and improved the segmentation effects; 3) Spectral and textural features were more important than geometric features in this study. A few main variables played a major role in the classification, while a large number of variables had either no significant effect or a negative effect on the classification results when the optimal feature subset was determined. This study provides a scientific basis and reference for the classification of various grassland types by remote sensing, including the data selection, image segmentation, feature selection, classifier selection, and parameter settings.

SALIENCY-GUIDED CHANGE DETECTION OF REMOTELY SENSED IMAGES USING RANDOM FOREST

Abstract. Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study.

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke, as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m2 and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations.

Cobia cage culture distribution mapping and carrying capacity assessment in Phu Quoc, Kien Giang province

Cobia fish cage is the most popular marine culture species raised in Phu Quoc Island, Vietnam. For its sustainable development, there is a need to determine the carrying capacity to avoid negative marine environmental impact in the future. This study was carried out to collect water samples each two months at the lowest and highest tides at four points around the farming area in Rach Vem, Phu Quoc Island, Kien Giang Province from February to October 2011. Water quality in cobia cage culture was surveyed to assess the environmental status of coastal aquaculture areas including seven parameters such as DO, COD, BOD, TSS, TN, TP and Chlorophyll-a. These parameters are suitable to rear cobia fish cage in this area. Nitrogen and phosphorus are considered as the principal nutrients produced by the cobia fish farm and affecting water environment. This study found that the carrying capacity for fish cage farming in the area is 290.96 to 727.81 tons (based on total nitrogen) and 428.64 to 1,383.88 tons (based on total phosphorus) from February to Au-gust 2011. The maximum number of cobia cages should be, based on total nitrogen, from 64 to 266 and, based on total phosphorus, from 94 to 253. Moreover, this study examined the possibility of remote sensing and geographic information system (GIS) technique based on Object-based Image Analysis (OBIA) method by THEOS imagery for mapping of cage culture facilities and detect the location for cobia cage culture in study area.&#x0D; Cá bớp nuôi lồng bè là một trong những loài cá nuôi phổ biến khu vực ven biển Phú Quốc, Việt nam. Nhằm phát triển bền vững vùng ven biển, đề tài thực hiện đã xác định và đánh giá hiện trạng môi trường nước và sức tải môi trường của nghề nuôi cá bớp lồng bè ở đảo Phú Quốc. Nghiên cứu này được thực hiện thông qua việc thu mẫu môi trường nước mỗi 2 tháng theo mức nước triều cao nhất và thấp nhất tại 4 điểm quanh khu vực nuôi tại ấp Rạch Vẹm, huyện Phú Quốc, tỉnh Kiên Giang từ tháng 02-10/2011. Chất lượng nước khu vực nuôi thủy sản cũng được khảo sát với 7 chỉ tiêu gồm DO, COD, BOD, TSS, TN, TP, Chlorophyll-a. Các thông số chất lượng môi trường này phù hợp nuôi cá bớp tại khu vực nghiên cứu dựa trên tiêu chuẩn chất lượng môi trường. Trong nghiên cứu này, đạm và lân là 2 thông số được sử dụng để tính toán sức tải môi trường. Sức tải môi trường được tính toán cho khu vực nuôi cá bớp dao động khoảng từ 290.96 tấn đến 727.81 tấn (tính trên hàm lượng đạm tổng số) và từ 428,64 tấn đến 1.383,88 tấn (tính trên hàm lượng lân tổng số) trong thời gian từ tháng 2 đến tháng 10 năm 2011. Số lượng tối đa lồng bè nuôi cá bớp khoảng từ 64 đến 266 (đạm tổng số) và từ 94 đến 253 (lân tổng số) dựa vào phân tích hồi quy tương quan. Bên cạnh đó, công nghệ viễn thám và hệ thống thông tin địa lý (GIS) đã được ứng dụng bằng cách sử dụng ảnh THEOS để xác định vị trí và phân bố không gian khu vực nuôi cá lồng bè dựa trên phương pháp phân tích đối tượng theo hướng (OBIA).

Automatic fuzzy object-based analysis of VHSR images for urban objects extraction

We present an automatic approach for object extraction from very high spatial resolution (VHSR) satellite images based on Object-Based Image Analysis (OBIA). The proposed solution requires no input data other than the studied image. Not input parameters are required. First, an automatic non-parametric cooperative segmentation technique is applied to create object primitives. A fuzzy rule base is developed based on the human knowledge used for image interpretation. The rules integrate spectral, textural, geometric and contextual object proprieties. The classes of interest are: tree, lawn, bare soil and water for natural classes; building, road, parking lot for man made classes. The fuzzy logic is integrated in our approach in order to manage the complexity of the studied subject, to reason with imprecise knowledge and to give information on the precision and certainty of the extracted objects. The proposed approach was applied to extracts of Ikonos images of Sherbrooke city (Canada). An overall total extraction accuracy of 80% was observed. The correctness rates obtained for building, road and parking lot classes are of 81%, 75% and 60%, respectively.

Imaginaire scientifique de Viollet-Le-Duc

Soil degradation on Alpine grasslands is triggered mainly by extreme topography, prevailing climate conditions and land use practices. Suitable monitoring tools are required to assess soil erosion with high temporal and spatial resolution. In this study, we present an unprecedented and comprehensive approach based on object-based image analysis (OBIA) to map and assess all occurring erosion processes within a catchment (Urseren Valley, Switzerland). Five high-resolution (0.25−0.5m) orthophotos with RGB spectral information (SwissImage) produced during a 16-yr period were analyzed. Soil erosion sites are classified according to their type (shallow landslide or sites with reduced vegetation cover affected by sheet erosion) or the triggering land use management impacts (haying, trampling) with the Overall Accuracy ranging between 78 and 88% (Kappa 0.65–0.81) for the different years. The area affected by soil erosion increases for all classes during the study period (2000–2016) by a total of 156 ± 18% (increase consisting of 3% shallow landslides, 5% livestock trails, 46% sheet erosion and 46% management effects). Slopes at lower elevations (<1800masl) are increasingly affected by livestock trails and sheet erosion caused by trampling and grazing as well as other management practices. For areas located above the agricultural land use, an increase in shallow landslides, as well as sheet erosion, can be observed. This points to climate change as a triggering factor of soil degradation, which has not been identified so far as a factor for soil erosion in the Urseren Valley. While OBIA yields conservative estimations mainly due to limitations of spatial resolutions, the method facilitates a comprehensive overview of the ongoing temporal and spatial development regarding soil degradation within the Urseren Valley.