A comparison of pixel-based decision tree and object-based Support Vector Machine methods for land-cover classification based on aerial images and airborne lidar data

Precisely monitoring land cover/use is crucial for urban environmental assessment and management. Various classification techniques such as pixel-based and object-based approaches have advantages and disadvantages. In this article, based on our experiment data from an unmanned platform carried lidar scanner system and camera, we explored and compared classi?cation accuracies of pixel-based decision tree (DT) and object-based Support Vector Machine (SVM) approaches. Lidar height information can improve classification accuracy based on either object-based SVM or pixel-based DT. From total classification accuracy, object-based SVM was higher than that of pixel-based DT classification, and total accuracy and kappa coefficient of the former were 92.71% and 0.899, respectively. However, pixel-based DT outperformed object-based SVM when classifying small ‘scatter’ tree along roads. Additionally, in order to evaluate the accuracy of pixel-based DT and object-based SVM, we added benchmark data of ISPRS to compare the classification results of two methods. Object-based SVM classification methods by combining aerial imagery with lidar height information can achieve higher classification accuracy. And, accurately extracting tree class of different landscape pattern should select appropriate machine-learning algorithms. Comparison of the results on two methods will provide a reference for selecting a particular classification approaches according to local conditions.     

Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains,California

Treatments to reduce forest fuels are often performed in forests to enhance forest health, regulate stand density, and reduce the risk of wildfires. Although commonly employed, there are concerns that these forest fuel treatments (FTs) may have negative impacts on certain wildlife species. Often FTs are planned across large landscapes, but the actual treatment extents can differ from the planned extents due to operational constraints and protection of resources (e.g. perennial streams, cultural resources, wildlife habitats). Identifying the actual extent of the treated areas is of primary importance to understand the environmental influence of FTs. Light detection and ranging (lidar) is a powerful remote-sensing tool that can provide accurate measurements of forest structures and has great potential for monitoring forest changes. This study used the canopy height model (CHM) and canopy cover (CC) products derived from multi-temporal airborne laser scanning (ALS) data to monitor forest changes following the implementation of landscape-scale FT projects. Our approach involved the combination of a pixel-wise thresholding method and an object-of-interest (OBI) segmentation method. We also investigated forest change using normalized difference vegetation index (NDVI) and standardized principal component analysis from multi-temporal high-resolution aerial imagery. The same FT detection routine was then applied to compare the capability of ALS data and aerial imagery for FT detection. Our results demonstrate that the FT detection using ALS-derived CC products produced both the highest total accuracy (93.5%) and kappa coefficient (κ) (0.70), and was more robust in identifying areas with light FTs. The accuracy using ALS-derived CHM products (the total accuracy was 91.6%, and the κ was 0.59) was significantly lower than that using ALS-derived CC, but was still higher than using aerial imagery. Moreover, we also developed and tested a method to recognize the intensity of FTs directly from pre- and post-treatment ALS point clouds.     

A new feature selection from lidar data and digital aerial images acquired for an urban/c environment using an ANFIS-based classification and a fuzzy rough set method

In this article, a new fuzzy rough set (FRS) method was proposed for extracting rules from an adaptive neuro-fuzzy inference system (ANFIS)-based classification procedure in order to select the optimum features. The proposed methodology was used to classify lidar data and digital aerial images acquired for an urban environment to detect four classes, including trees, buildings, roads, and natural grounds. In this regard, 16 potentially primary features were produced for classification using the lidar data and the digital aerial images. The training and checking inputs of the proposed ANFIS were collected from the generated features for further training and evaluation processes. Also, the fuzzy c-mean clustering algorithm was used to initialize the fuzzy inference system of the proposed ANFIS-based classification method. By considering all states of fuzzy rules for each training input, the fuzzy rule with the maximum firing value was selected. Accordingly, these fuzzy rules were used as the inputs of the Rough Set Theory. Accordingly, the optimum features were acquired by the basic minimal covering algorithm as the rule induction method. To validate our proposed methodology, the procedure of classification was repeated by the achieved optimum features. The results showed that the classification using the optimum features has reached better overall accuracy than those achieved by using the 16 potentially primary features. Also, comparing the results of our proposed methodology with the other well-known genetic-algorithm-based feature selection methods indicated the significance of the proposed FRS method to select optimum features with high accuracy in a short running time.     

Building detection in an urban area using lidar data and QuickBird imagery

This article presents a hierarchical approach to detect buildings in an urban area through the combined usage of lidar data and QuickBird imagery. A normalized digital surface model (nDSM) was first generated on the basis of the difference between a digital surface model and the corresponding digital terrain model. Then, ground objects were removed according to a height threshold. In consideration of the relief displacement effect in very high resolution remote-sensing imagery, we segmented the nDSM by the region-growing method and used the overlap ratio to avoid over-removing building objects. Finally, the region size and spatial relation of trees and buildings were used to filter out trees occluded by buildings based on an object-based classification. Compared with previous methods directly using the normalized difference vegetation index (NDVI), our method improved the completeness from 85.94% to 90.20%. The overall accuracy of the buildings detected using the proposed method can be up to 94.31%, indicating the practical applicability of the method.     

Extraction of earthquake-induced collapsed buildings using very high-resolution imagery and airborne lidar data

Extraction of urban building damage caused by earthquake disasters, from very-high-resolution (VHR) satellite imagery and related geospatial data, has been widely studied in the past decade. In this study, a multi-stage collapsed building detection method, using bi-temporal (pre- and post-earthquake) VHR images and post-earthquake airborne light detection and ranging (lidar) data, is proposed. Ground objects that are intact and significantly different from collapsed buildings, such as intact buildings, pavements, shadows, and vegetation, were first extracted using the post-event VHR image and lidar data and masked out. Collapsed buildings were then extracted by classifying the combined bi-temporal VHR images and texture images of the remaining area using a one-class classifier, the One-Class Support Vector Machine (OCSVM). A post-processing procedure was adopted to refine the obtained result. The proposed method was quantitatively evaluated and compared to two existing methods in Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010. In the two comparative methods, data for the whole study area were directly used. In the first method, collapsed buildings were extracted directly using the OCSVM, while in the second method, buildings and pavements were removed from the extraction result of the first method. The results showed that the proposed method significantly outperformed the existing methods, with increases of 21% and 40%, respectively, in the kappa coefficient. The proposed method provides a fast and reliable method to detect collapsed urban buildings caused by earthquake disasters, and could also be applied to other study areas using similar data combinations.     

基于形状与纹理特征的显微图像识别

为了实现对空气中的致敏花粉信息进行自动化统计,针对上海地区典型气传致敏花粉的光学显微镜图像,提出了基于形状和纹理特征的识别方法。对图像中分割得到的花粉区域,使用全局形状描述和傅里叶描述子提取形状信息,灰度共生矩阵提取纹理特征,并且构建k近邻分类器进行识别。选用桑科56例、禾本科25例和松科60例共141例实验样本,分别可以实现91%、88%和98%分类准确率。实验结果表明,该方法可以初步实现对花粉显微图像的分割和识别,为花粉的自动识别系统打下基础。     

Automatic extraction of roads from aerial images based on scale space and snakes

Abstract. We propose a new approach for automatic road extraction from aerial imagery with a model and a strategy mainly based on the multi-scale detection of roads in combination with geometry-constrained edge extraction using snakes. A main advantage of our approach is, that it allows for the first time a bridging of shadows and partially occluded areas using the heavily disturbed evidence in the image. Additionally, it has only few parameters to be adjusted. The road network is constructed after extracting crossings with varying shape and topology. We show the feasibility of the approach not only by presenting reasonable results but also by evaluating them quantitatively based on ground truth. Received: 22 July 1999 / Accepted: 20 March 2000     

Aerial object ranging based on goniometric data in an airborne single-position passive optoelectronic system

A method is proposed for determining motion parameters of an aerial object using a passive optoelectronic system when an aircraft (AC) performs a special maneuver. The method is based on measuring the azimuth and elevation of the aerial object, as well as the AC’s own coordinates at three path points: two measurements on the straight-and-level segment and one measurement after the maneuver. The method does not violate the tactical situation and makes it possible to maintain optical contact with the object during the maneuver. The method is modeled. The accuracy characteristics are estimated. The validation procedure for determining an aerial object’s motion parameters is proposed.     

Removal of azimuth ambiguities and detection of a ship: using polarimetric airborne C-band SAR images

Synthetic aperture radar (SAR) imagery from the sea can contain ships and their ambiguities. The ambiguities are visually identifiable due to their high intensities in the low radar backscatter background of sea environments and can be mistaken as ships, resulting in false alarms in ship detection. Analysing polarimetric characteristics of ships and ambiguities, we found that (a) backscattering from a ship consisted of a mixture of single-bounced, double-bounced and depolarized or diffused scattering types due to its complex physical structure; (b) that only a strong single- or double-bounce scatterer produced ambiguities in azimuth that look like relatively strong double- or single-bounce scatterers, respectively; and (c) that eigenvalues corresponding to the single- or double-bounce scattering mechanisms of the ambiguities were high but the eigenvalue corresponding to the depolarized scattering mechanisms of the ambiguities was low. With these findings, we proposed a ship detection method that applies the eigenvalue to differentiate the ship target and azimuth ambiguities. One set of C-band JPL AIRSAR (Jet Propulsion Laboratory Airborne Synthetic Aperture Radar) polarimetric data from the sea have been chosen to evaluate the method that can effectively delineate ships from their azimuth ambiguities.     

The compression of a sequence of satellite images based on change detection

A 'loss-effective' compression method which based on the change detection of raw image data is proposed for dealing with a sequence of satellite images. The average compression ratio we gained, compared with some typical satellite image formats, is about 2:1 to 3 :1. This sounds not so impressive when compared with the most current compression techniques which used in multimedia processing. However, some information will be lost in those methods, while our approach is information-loss effective, which is crucial for further satellite image analysis. Moreover, the framework can be combined with different compression algorithms to obtain different trade-offs between the compression ratio and the computation time. Experimental results based on real satellite images are included. Finally, other issues including the further optimization of the methods and some other possible applications of the method are discussed.     

基于NSCT及改进Canny的图像边缘检测

为进一步提取丰富的图像边缘信息,提出了一种基于非下采样Contourlet变换(Nonsubsampled Contourlet Transform,NSCT)及改进Canny的图像边缘检测方法。该方法是将图像进行NSCT多尺度分解,得到低频和高频子带。首先对低频子带使用改进Canny算子提取低频轮廓,再使用非线性函数对高频子带信息中各尺度各方向上的系数进行调整,实现边缘增强和噪声抑制,最后将NSCT域尺度内和尺度间的检测结果相融合来得到完整的边缘图像。实验结果表明,相比Sobel、Canny算子和现有的NSCT边缘检测方法,文中方法具有更好的边缘检测效果,边缘定位准确、完整、连续、细节丰富。     

Change detection in SAR images based on matrix factorisation and a Bayes classifier

Change detection in synthetic aperture radar (SAR) images can be made as a matrix factorisation model, and it can detect the changes based on the foreground information in the image. However, these methods cannot obtain satisfactory results in the change detection of SAR images because reliable background data are often not available. In this article, we propose a matrix factorisation model based on a naïve Bayes classifier to explore the low-rank and sparse information, and then detect the changes in SAR images. The factorisation model of the low-rank and sparse matrix extracts both background and foreground information from images. From the low-rank and sparse matrices, we can get the background and foreground information recovered, respectively. Then by computing the mean and variance matrix of the unchanged and changed region information, we will obtain the statistical features. The statistical features are then used to build a naïve Bayes classifier, which is used to distinguish the change detection results, and all of them are based on the acquired data distribution. The experiments, which are based on four real data sets, indicate that the approach gets a better performance than some other state-of-the-art algorithms.     

Objects based change detection in a pair of gray-level images

The goal of the presented change detection algorithm is to extract objects that appear in only one of two input images. A typical application is surveillance, where a scene is captured at different times of the day or even on different days. In this paper we assume that there may be a significant noise or illumination differences between the input images. For example, one image may be captured in daylight while the other was captured during night with an infrared device. By using a connectivity analysis along gray-level technique, we extract significant blobs from both images. All the extracted blobs are candidates to be classified as changes or part of a change. Then, the candidate blobs from both images are matched. A blob from one image that does not satisfy the matching criteria with its corresponding blob from the other image is considered as an object of change. The algorithm was found to be reliable, fast, accurate, and robust even under extreme changes in illumination and some distortion of the images. The performance of the algorithm is demonstrated using real images. The worst-case time complexity of the algorithm is almost linear in the image size. Therefore, it is suitable for real-time applications.     

Effects of multiple scattering on time- and depth-resolved signals in airborne lidar systems

Abstract

A semianalytic Monte Carlo radiative transfer model (SALMON) is employed to probe the effects of multiple-scattering events on the time- and depth-resolved lidar signals from homogeneous aqueous media. The effective total attenuation coefficients in the single-scattering approximation are determined as functions of dimensionless parameters characterizing the lidar system and the medium. Results show that single-scattering events dominate when these parameters are close to their lower bounds and that when their values exceed unity multiple-scattering events dominate.     

基于机载SAR图像的海面船只实时检测分类应用架构

合成孔径雷达(SAR)被广泛应用于军事侦察、海洋监测、灾害应急评估等各类应用中.其中,SAR海面船只检测分类是SAR海洋应用的重要一环.限于SAR特殊的成像机理,SAR图像中的目标特征多变,导致基于SAR图像的目标分类应用进展缓慢.机载SAR因其实时、便捷的部署和使用方式得到了快速发展,基于机载SAR图像的海面船只检测...     

Change detection method for remote sensing images based on an improved Markov random field

The fixed weights between the center pixel and neighboring pixels are used in the traditional Markov random field for change detection, which will easily cause the overuse of spatial neighborhood information. Besides the traditional label field cannot accurately identify the spatial relations between neighborhood pixels. To solve these problems, this study proposes a change detection method based on an improved MRF. Linear weights are designed for dividing unchanged, uncertain and changed pixels of the difference image, and spatial attraction model is introduced to refine the spatial neighborhood relations, which aims to enhance the accuracy of spatial information in MRF. The experimental results indicate that the proposed method can effectively enhance the accuracy of change detection.     

Capturing and tracking of building area based on structure saliency in airborne remote sensing video

Dear editor,Object capturing and tracking in airborne remote sensing video is one of the hottest research topics,which is widely used in disaster monitoring and rescue and vehicle tracking.Because of the high realtime requirement in the above applications,the fast object capturing and stable tracking have significant and practical value.     

基于PC104的机载数据加卸载设备的设计

针对目前航空兵部队机载电子对抗设备种类繁多、数据加卸载复杂的问题,提出了将机载设备作为PC104嵌入式计算机外部设备的设计方案,利于多路I/O接口技术,解决了外场数据加卸载以及内场数据传送的设备通用性问题,实现了数据加卸载设备的微型化、通用化.     

Evaluation of LANDSAT and airborne multispectral data and aerial photographs for mapping forest features and phenomena in a part of the Godavari basin

The automatic interpretation of multispectral digital data obtained from LANDSAT as well as from an airborne multispectral scanner using an interactive computer system and visual interpretation of colour composites of LANDSAT imagery and aerial photographs of a dry deciduous forest tract were used for evaluating the discrimination capabilities of each technique and for comparative evaluation. While visual interpretation of LANDSAT imagery could give only general information, such as contiguity of vegetation cover, digital analysis of the same yielded more detailed information, such as teak-bearing and non-teak-bearing regions. The analysis of airborne multispectral data, in the present state of the art, for performing forest surveys and making maps is limited. Aerial photographs are very useful for mapping forest land features and stock, which can be done more reliably than could be done by ground surveys. Infrared photographs show better promise in mapping forest features. The integration of multitemporal data and the incorporation of digitized additional information into the data stream for the improvement of digital analysis are suggested. Acquisition of data including aerial photographs for general surveys during a period prior to leaf fall in a deciduous forest is also recommended.     

Retrieval of forest canopy attributes based on a geometric-optical model using airborne LiDAR and optical remote-sensing data

In the retrieval of forest canopy attributes using a geometric-optical model, the spectral scene reflectance of each component should be known as prior knowledge. Generally, these reflectances were acquired by a foregone survey using an analytical spectral device. This article purposed to retrieve the forest structure parameters using light detection and ranging (LiDAR) data, and used a linear spectrum decomposition model to determine the reflectances of the spectral scene components, which are regarded as prior knowledge in the retrieval of forest canopy cover and effective plant area index (PAI_e) using a simplified Li–Strahler geometric-optical model based on a Satellites Pour l'Observation de la Terre 5 (SPOT-5) high-resolution geometry (HRG) image. The airborne LiDAR data are first used to retrieve the forest structure parameters and then the proportion of the SPOT pixel not covered by crown or shadow K_g of each pixel in the sample was calculated, which was used to extract the reflectances of the spectral scene components by a linear spectrum decomposition model. Finally, the forest canopy cover and PAI_e are retrieved by the geometric-optical model. As the acquired time of SPOT-5 image and measured data has a discrepancy of about 2 months, the retrieved result of forest canopy cover needs a further validation. The relatively high value of R ² between the retrieval result of PAI_e and the measurements indicates the efficiency of our methods.