Abstract:The increased feature space available in object-based classification environments (e.g., extended spectral feature sets per object, shape properties, or textural features) has a high potential of improving classifications. However, the availability of a large number of derived features per segmented object can also lead to a time-consuming and subjective process of optimizing the feature subset. The objectives of this study are to evaluate the effect of the advanced feature selection methods of popular supervised classifiers (Support Vector Machines (SVM) and Random Forest (RF)) for the example of object-based mapping of an agricultural area using Unmanned Aerial Vehicle (UAV) imagery, in order to optimize their usage for object-based agriculture pattern recognition tasks. In this study, several advanced feature selection methods were divided into both types of classifiers (SVM and RF) to conduct further evaluations using five feature-importance-evaluation methods and three feature-subset-evaluation methods. A visualization method was used to measure the change pattern of mean classification accuracy with the increase of features used, and a two-tailed t-test was used to determine the difference between two population means for both repeated ten classification accuracies. This study mainly contribute to the uncertainty analysis of feature selection for object-based classification instead of the per-pixel method. The results highlight that the RF classifier is relatively insensitive to the number of input features, even for a small training set size, whereby a negative impact of feature set size on the classification accuracy of the SVM classifier was observed. Overall, the SVM Recursive Feature Elimination (SVM-RFE) seems to be an appropriate method for both groups of classifiers, while the Correlation-based Feature Selection (CFS) is the best feature-subset-evaluation method. Most importantly, this study verified that feature selection for both classifiers is crucial for the evolving field of Object-based Image Analysis (OBIA): It is highly advisable for feature selection to be performed before object-based classification, even though an adverse impact could sometimes be observed from the wrapper methods.

, "Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery," J. Appl. Remote Sens. 12(2), 025010 (2018), doi: 10.1117/1.JRS.12.025010. Abstract. Convolutional neural network (CNN) has shown great success in computer vision tasks, but their application in land-use type classifications within the context of object-based image analysis has been rarely explored, especially in terms of the identification of irregular segmentation objects. Thus, a blocks-based object-based image classification (BOBIC) method was proposed to carry out end-to-end classification for segmentation objects using CNN. Specifically, BOBIC takes advantage of CNN to automatically extract complex features from the original image data, thereby avoiding the uncertainty caused by the manual extraction of features in OBIC. Additionally, OBIC compensates for the shortcomings of CNN whereby it is difficult to delineate a clear right boundary for ground objects at the pixel level. Using three high-resolution test images, the proposed BOBIC was compared with support vector machine (SVM) and random forest (RF) classifiers, and then, the effect of image blocks and mixed objects on classification accuracy was evaluated for the proposed BOBIC. Compared with conventional SVM and RF classifiers, the inclusion of CNN improved the OBIC classification performance substantially (5% to 10% increases in overall accuracy), and it also alleviated the effect derived from mixed objects.

Despite increases in the spatial resolution of satellite imagery prompting interest in object-based image analysis, few studies have used object-based methods for monitoring changes in coral reefs. This study proposes a high accuracy object-based change detection (OBCD) method intended for coral reef environment, which uses QuickBird and WorldView-2 images. The proposed methodological framework includes image fusion, multi-temporal image segmentation, image differencing, random forests models, and object-area-based accuracy assessment. For validation, we applied the method to images of four coral reef study sites in the South China Sea. We compared the proposed OBCD method with a conventional pixel-based change detection (PBCD) method by implementing both methods under the same conditions. The average overall accuracy of OBCD exceeded 90%, which was approximately 20% higher than PBCD. The OBCD method was free from salt-and-pepper effects and was less prone to images misregistration in terms of change detection accuracy and mapping results. The object-area-based accuracy assessment reached a higher overall accuracy and per-class accuracy than the object-number-based and pixel-number-based accuracy assessment. methods for coral reef change detection. However, because of the limitations of medium-resolution sensors such as Landsat and SPOT, it is difficult to distinguish coral reef geomorphological dynamics from sea level rise [10], and to detect changes on the scale of a few meters in coral reef habitats [11]. With continued refinement of the spatial resolution of satellite imagery, conventional per-pixel methods have been found susceptible to a number of challenges in relation to change detection, including image misregistration [6,12] and salt-and-pepper effects [13,14]. Coral reef images, lack of distinct and stable texture features, are difficult to be accurately registered to each other [15], which makes the traditional pixel-based approach less promising in coral reef change detection using high-resolution images.Recent years have seen an increase in the number of studies using object-based image analysis (OBIA) [13]. OBIA has also been applied in coral reef environment, from geomorphological mapping to benthic community discrimination [16][17][18]. OBIA represents an effective combination of both the contextual analysis of visual interpretation and the quantitative analysis of the pixel-based method [19]. It has been proven that image registration error greatly affects the per-pixel change detection accuracy while the object-based method is less sensitive to image misregistration [20,21]. However, to the best of our knowledge, few studies have used object-based change detection (OBCD) methods in coral reef change detection. Generally, there are two possible strategies for OBCD methods: post-classification comparison and multi-temporal image object analysis [22]. The essence of the post-classification comparison approach lies in the initial classification, i.e., images acquired at different times are classifi...

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When estimating national PM2.5 concentrations, the results of traditional interpolation algorithms are unreliable due to a lack of monitoring sites and heterogeneous spatial distributions. PM2.5 spatial distribution is strongly correlated to elevation, and the information diffusion algorithm has been shown to be highly reliable when dealing with sparse data interpolation issues. Therefore, to overcome the disadvantages of traditional algorithms, we proposed a method combining elevation data with the information diffusion algorithm. Firstly, a digital elevation model (DEM) was used to segment the study area into multiple scales. Then, the information diffusion algorithm was applied in each region to estimate the ground PM2.5 concentration, which was compared with estimation results using the Ordinary Kriging and Inverse Distance Weighted algorithms. The results showed that: (1) reliable estimate at local area was obtained using the DEM-assisted information diffusion algorithm; (2) the information diffusion algorithm was more applicable for estimating daily average PM2.5 concentrations due to the advantage in noise data; (3) the information diffusion algorithm required less supplementary data and was suitable for simulating the diffusion of air pollutants. We still expect a new comprehensive model integrating more factors would be developed in the future to optimize the interpretation accuracy of short time observation data.