Segmentation and classification of individual tree crowns

By segmentation and classification of individual tree crowns in high spatial resolution aerial images, information about the forest can be automatically extracted. Segmentation is about finding the individual tree crowns and giving each of them a unique label. Classification, on the other hand, is about recognising the species of the tree. The information of each individual tree in the forest increases the knowledge about the forest which can be useful for managements, biodiversity assessment, etc. Different algorithms for segmenting individual tree crowns are presented and also compared to each other in order to find their strengths and weaknesses. All segmentation algorithms developed in this thesis focus on preserving the shape of the tree crown. Regions, representing the segmented tree crowns, grow according to certain rules from seed points. One method starts from many regions for each tree crown and searches for the region that fits the tree crown best. The other methods start from a set of seed points, representing the locations of the tree crowns, to create the regions. The segmentation result varies from 73 to 95 % correctly segmented visual tree crowns depending on the type of forest and the method. The former value is for a naturally generated mixed forest and the latter for a non-mixed forest. The classification method presented uses shape information of the segments and colour information of the corresponding tree crown in order to decide the species. The classification method classifies 77 % of the visual trees correctly in a naturally generated mixed forest, but on a forest stand level the classification is over 90 %

Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and modeling. In addressing scene understanding problem, we present a framework for semantic segmentation and object recognition on urban video sequence only using dense depth maps recovered from the video. Five view-independent 3D features that vary with object class are extracted from dense depth maps and used for segmenting and recognizing different object classes in street scene images. We demonstrate a scene parsing algorithm that uses only dense 3D depth information to outperform using sparse 3D or 2D appearance features. In addressing image enhancement problem, we present a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale internet photo collections (IPCs). By augmenting personal 2D images with 3D information reconstructed from IPCs, we address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. In addressing 3D reconstruction and modeling problem, we focus on parametric modeling of flower petals, the most distinctive part of a plant. The complex structure, severe occlusions and wide variations make the reconstruction of their 3D models a challenging task. We overcome these challenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, each segmented petal is fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned 3D exemplar petals. Novel constraints based on botany studies are incorporated into the fitting process for realistically reconstructing occluded regions and maintaining correct 3D spatial relations. The main contribution of the dissertation is in the intelligent usage of 3D depth information on solving traditional challenging vision/graphics problems. By developing some advanced algorithms either automatically or with minimum user interaction, the goal of this dissertation is to demonstrate that computed 3D depth behind the multiple images contains rich information of the visual world and therefore can be intelligently utilized to recognize/ understand semantic meanings of scenes, efficiently enhance and augment single 2D images, and reconstruct high-quality 3D models

Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

3D machine vision system for robotic weeding and plant phenotyping

The need for chemical free food is increasing and so is the demand for a larger supply to feed the growing global population. An autonomous weeding system should be capable of differentiating crop plants and weeds to avoid contaminating crops with herbicide or damaging them with mechanical tools. For the plant genetics industry, automated high-throughput phenotyping technology is critical to profiling seedlings at a large scale to facilitate genomic research. This research applied 2D and 3D imaging techniques to develop an innovative crop plant recognition system and a 3D holographic plant phenotyping system. A 3D time-of-flight (ToF) camera was used to develop a crop plant recognition system for broccoli and soybean plants. The developed system overcame the previously unsolved problems caused by occluded canopy and illumination variation. Both 2D and 3D features were extracted and utilized for the plant recognition task. Broccoli and soybean recognition algorithms were developed based on the characteristics of the plants. At field experiments, detection rates of over 88.3% and 91.2% were achieved for broccoli and soybean plants, respectively. The detection algorithm also reached a speed over 30 frame per second (fps), making it applicable for robotic weeding operations. Apart from applying 3D vision for plant recognition, a 3D reconstruction based phenotyping system was also developed for holographic 3D reconstruction and physical trait parameter estimation for corn plants. In this application, precise alignment of multiple 3D views is critical to the 3D reconstruction of a plant. Previously published research highlighted the need for high-throughput, high-accuracy, and low-cost 3D phenotyping systems capable of holographic plant reconstruction and plant morphology related trait characterization. This research contributed to the realization of such a system by integrating a low-cost 2D camera, a low-cost 3D ToF camera, and a chessboard-pattern beacon array to track the 3D camera\u27s position and attitude, thus accomplishing precise 3D point cloud registration from multiple views. Specifically, algorithms of beacon target detection, camera pose tracking, and spatial relationship calibration between 2D and 3D cameras were developed. The phenotypic data obtained by this novel 3D reconstruction based phenotyping system were validated by the experimental data generated by the instrument and manual measurements, showing that the system has achieved measurement accuracy of more than 90% for most cases under an average of less than five seconds processing time per plant

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Plants Detection, Localization and Discrimination using 3D Machine Vision for Robotic Intra-row Weed Control

Weed management is vitally important in crop production systems. However, conventional herbicide-based weed control can lead to negative environmental impacts. Manual weed control is laborious and impractical for large scale production. Robotic weeding offers a possibility of controlling weeds precisely, particularly for weeds growing close to or within crop rows. The fusion of two-dimensional textural images and three-dimensional spatial images to recognize and localize crop plants at different growth stages were investigated. Images of different crop plants at different growth stages with weeds were acquired. Feature extraction algorithms were developed, and different features were extracted and used to train plant and background classifiers, which also addressed the problems of canopy occlusion and leaf damage. Then, the efficacy and accuracy of the proposed methods in classification were demonstrated by experiments. Currently, the algorithms were only developed and tested for broccoli and lettuce. For broccoli plants, the crop plants detection true positive rate was 93.1%, and the false discover rate was 1.1%, with the average crop-plant-localization error of 15.9 mm. For lettuce plants, the crop plants detection true positive rate was 92.3%, and the false discover rate was 4.0%, with the average crop-plant-localization error of 8.5 mm. The results have shown that 3D imaging based plant recognition algorithms are effective and reliable for crop/weed differentiation