A hierarchical Dempster-Shafer evidence combination framework for urban area land cover classification

This paper presents a novel evidence combination framework for urban area land cover classification by using Light Detection And Ranging (LIDAR) data fused with co-registered near infrared and color images. The newly developed combination framework is built with a hierarchical structure involving an improved Dempster-Shafer (DS) theory of evidence for decision making. In the framework, a fuzzy basic probability assignment (BPA) function with fuzzy classes is firstly established based on the DS theory of evidence, and a probability is then assigned to each data source, that is derived from the original airborne LIDAR and the co-registered images. Secondly, an interesting approach is to introduce noise removal in an interim stage at the output of the probability distribution, and then the probability assigned to each data source is redistributed with a designated rule. Finally, a decision is made based on a “maximum normal support” rule, leading to the classification results. The proposed framework has been tested on two datasets. The testing results have shown that it can dramatically reduce the computational time in the classification process, and significantly improve the classification accuracy, i.e. 8.22% on Test 1 and 5.76% on Test 2 compared to the basic DS method. Due to its non-iterative and unsupervised nature, the proposed method is fast in computation, does not require training samples, and has achieved high classification accuracy

水中環境における光学画像の画質改善に関する研究

Since the 1960s, autonomous underwater vehicles (AUVs) and unmanned underwater vehicles (UUVs) have been used for deep-sea exploration. Sonar sensors also have been extensively used to detect and recognize objects in oceans. Although sonar sensors are suitable for long-range distance imaging, due to the principles of acoustic imaging, sonar images are low signal to noise ratio, low resolution and no colors. In order to acquire more detail information of underwater object, a short-range imaging system is required. In this situation, a photo vision sensor is used reasonably.However, the low contrast and color distortion of underwater images are still the major issues for practical applications. Therefore, this thesis will concentrate on the underwater optical images quality improvement.Although the underwater optical imaging technology has made a great progress, the recognition of underwater objects is still a challenging subject nowadays. Different from the normal images, underwater images suffer from poor visibility due to the medium scattering and light distortion. First of all, capturing good quality images in underwater circumstance is difficult, mostly due to attenuation caused by light that is reflected from a surface and is deflected and scattered by particles. Secondly, absorption substantially reduces the light energy. The random attenuation of the light mainly causes the haze appearance along with the part of the light scattered back from the water. In particular, an underwater object which 10 meters away from camera lens is almost indistinguishable because of light absorption. Furthermore, when the artificial light is employed, it can cause a distinctive footprint on the seafloor.In order to obtain high quality underwater images that can be adapted to the traditional image identification algorithms, this work aimed to construct an underwater image processing framework. Due to the special characteristic of underwater images,segment the image to several parts before directly perform a subject identification is thought an efficient way. And for obtaining a good underwater image segment result, the work to improve the quality of the image is necessary. Such work contains image enhancement, color correction and noise reduction, etc. The experiments demonstrate that the proposed methods produced visually pleasing results, and the numerical image quality assessment also proved the effectiveness of this proposal. The organization of this thesis is as follows.Chapter 1 briefly reviews the characteristics and types of acoustic imaging and optical imaging technologies in ocean. The traditional underwater imaging models and the issues of recent underwater imaging systems are also introduced.Chapter 2 describes a novel underwater image enhancement method. The transmission is estimated by the proposed dual-channel prior. Then a robust locally adaptive filter algorithm for enhancing underwater images is used. In addition, theartificial light removal method is also proposed. Compared with the traditional methods, the proposed method obtains better images.Chapter 3 presents a color correction method to recover the distorted image colors. In the experiments, the proposed method recovers the distorted colors in real-time. The color corrected images have a reasonable noise level in their dark regions, and the global contrast is also well improved.Chapter 4 describes two methods for image segmentation. The first one is the automatic clustering Weighted Fuzzy C Means (WFCM) based segmentation method. It automatically obtains a reasonable clustering result for the underwater images with simple texture. The second method is fast Active Contour Model (ACM) based image segmentation method, which dramatically improves the calculation speed. Compare with the traditional methods, the processing speed is improved by over 10 times.Chapter 5 presents the conclusions of this work, and points out some future researchdirections.九州工業大学博士学位論文 学位記番号：工博甲第398号 学位授与年月日：平成27年9月25日1 INTRODUCTION|2 IMAGE ENHANCEMENT|3 COLOR CORRECTION|4 IMAGE SEGMENTATION|5 CONCLUSIONS九州工業大学平成27年

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

Predicting spatiotemporal yield variability to aid arable precision agriculture in New Zealand : a case study of maize-grain crop production in the Waikato region : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agriculture and Horticulture at Massey University, Palmerston North, New Zealand

Precision agriculture attempts to manage within-field spatial variability by applying suitable inputs at the appropriate time, place, and amount. To achieve this, delineation of field-specific management zones (MZs), representing significantly different yield potentials are required. To date, the effectiveness of utilising MZs in New Zealand has potentially been limited due to a lack of emphasis on the interactions between spatiotemporal factors such as soil texture, crop yield, and rainfall. To fill this research gap, this thesis aims to improve the process of delineating MZs by modelling spatiotemporal interactions between spatial crop yield and other complementary factors. Data was collected from five non-irrigated field sites in the Waikato region, based on the availability of several years of maize harvest data. To remove potential yield measurement errors and improve the accuracy of spatial interpolation for yield mapping, a customised filtering algorithm was developed. A supervised machine-learning approach for predicting spatial yield was then developed using several prediction models (stepwise multiple linear regression, feedforward neural network, CART decision tree, random forest, Cubist regression, and XGBoost). To provide insights into managing spatiotemporal yield variability, predictor importance analysis was conducted to identify important yield predictors. The spatial filtering method reduced the root mean squared errors of kriging interpolation for all available years (2014, 2015, 2017 and 2018) in a tested site, suggesting that the method developed in R programme was effective for improving the accuracy of the yield maps. For predicting spatial yield, random forest produced the highest prediction accuracies (R² = 0.08 - 0.50), followed by XGBoost (R² = 0.06 - 0.39). Temporal variables (solar radiation, growing degree days (GDD) and rainfall) were proven to be salient yield predictors. This research demonstrates the viability of these models to predict subfield spatial yield, using input data that is inexpensive and readily available to arable farms in New Zealand. The novel approach employed by this thesis may provide opportunities to improve arable farming input-use efficiency and reduce its environmental impact