Segmentation of Sedimentary Grain in Electron Microscopy Image

This paper describes a novel method developed for the segmentation of sedimentary grains in electron microscopy images. The algorithm utilizes the approach of region splitting and merging. In the splitting stage, the marker-based watershed segmentation is used. In the merging phase, the typical characteristics of grains in electron microscopy images are exploited for proposing special metrics, which are then used during the merging stage to obtain a correct grain segmentation. The metrics are based on the typical intensity changes on the grain borders and the compact shape of grains. The experimental part describes the optimal setting of parameter in the splitting stage and the overall results of the proposed algorithm tested on available database of grains. The results show that the proposed technique fulfills the requirements of its intended application

Detecting Invasive Insects with Unmanned Aerial Vehicles

A key aspect to controlling and reducing the effects invasive insect species have on agriculture is to obtain knowledge about the migration patterns of these species. Current state-of-the-art methods of studying these migration patterns involve a mark-release-recapture technique, in which insects are released after being marked and researchers attempt to recapture them later. However, this approach involves a human researcher manually searching for these insects in large fields and results in very low recapture rates. In this paper, we propose an automated system for detecting released insects using an unmanned aerial vehicle. This system utilizes ultraviolet lighting technology, digital cameras, and lightweight computer vision algorithms to more quickly and accurately detect insects compared to the current state of the art. The efficiency and accuracy that this system provides will allow for a more comprehensive understanding of invasive insect species migration patterns. Our experimental results demonstrate that our system can detect real target insects in field conditions with high precision and recall rates.Comment: IEEE ICRA 2019. 7 page

Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation

Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management. However, the dense distribution and various sizes of aquacultures make it challenging to accurately extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric decomposition algorithms were applied to extract 13 polarimetric scattering features. Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index. Subsequently, to reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground functions, the results of which were then used in the marker-controlled watershed algorithm to obtain refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers into three categories (aquaculture, background and dikes) was applied to the marker function. In addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a morphological operation that was conducted for label augmentation. An experimental investigation performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were introduced, our proposed method performed better than standard UNet++ in aquaculture extraction, achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The experimental results indicate that the proposed method can handle the adhesion of both adjacent objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries

GIS-based urban land use characterization and population modeling with subpixel information measured from remote sensing data

This dissertation provides deeper understanding on the application of Vegetation-Impervious Surface-Soil (V-I-S) model in the urban land use characterization and population modeling, focusing on New Orleans area. Previous research on the V-I-S model used in urban land use classification emphasized on the accuracy improvement while ignoring the discussion of the stability of classifiers. I developed an evaluation framework by using randomization techniques and decision tree method to assess and compare the performance of classifiers and input features. The proposed evaluation framework is applied to demonstrate the superiority of V-I-S fractions and LST for urban land use classification. It could also be applied to the assessment of input features and classifiers for other remote sensing image classification context. An innovative urban land use classification based on the V-I-S model is implemented and tested in this dissertation. Due to the shape of the V-I-S bivariate histogram that resembles topological surfaces, a pattern that honors the Lu-Weng’s urban model, the V-I-S feature space is rasterized into grey-scale image and subsequently partitioned by marker-controlled watershed segmentation, leading to an urban land use classification. This new approach is proven to be insensitive to the selection of initial markers as long as they are positioned around the underlying watershed centers. This dissertation links the population distribution of New Orleans with its physiogeographic conditions indicated by the V-I-S sub-pixel composition and the land use information. It shows that the V-I-S fractions cannot be directly used to model the population distribution. Both the OLS and GWR models produced poor model fit. In contrast, the land use information extracted from the V-I-S information and LST significantly improved regression models. A three-class land use model is fitted adequately. The GWR model reveals the spatial nonstationarity as the relationship between the population distribution and the land use is relatively poor in the city center and becomes stronger towards the city fringe, depicting a classic urban concentric pattern. It highlighted that New Orleans is a complex metropolitan area, and its population distribution cannot be fully modeled with the physiogeographic measurements

Fuzzy C-Means Clustering Based on Improved Marked Watershed Transformation

Currently, the fuzzy c-means algorithm plays a certain role in remote sensing image classification. However, it is easy to fall into local optimal solution, which leads to poor classification. In order to improve the accuracy of classification, this paper, based on the improved marked watershed segmentation, puts forward a fuzzy c-means clustering optimization algorithm. Because the watershed segmentation and fuzzy c-means clustering are sensitive to the noise of the image, this paper uses the adaptive median filtering algorithm to eliminate the noise information. During this process, the classification numbers and initial cluster centers of fuzzy c-means are determined by the result of the fuzzy similar relation clustering. Through a series of comparative simulation experiments, the results show that the method proposed in this paper is more accurate than the ISODATA method, and it is a feasible training method

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques