Mapping dry/wet snow cover in the Indian Himalayas using IRS multispectral imagery

Application of remote sensing data has been made to differentiate between dry/wet snows in a glacierized basin. The present study has been carried out in the Gangotri glacier, Himalayas, using IRS-LISS-III multispectral data for the period March-November 2000 and the digital elevation model. The methodology involves conversion of satellite sensor data into reflectance values, computation of NDSI, determination of the boundary between dry/wet snows from spectral response data, and threshold slicing of the image data. The areas of dry snow cover and wet snow cover for different dates of satellite overpasses have been computed. The dry snow area has been compared with non-melting area obtained from the temperature lapse rate method, and the two are found to be in close mutual correspondence (< 15%). It is observed that there occur four water-bearing zones in the glacierized basin: dry snow zone, wet snow zone, exposed glacial ice and moraine-covered glacial ice, each of which possesses unique hydrological characteristics and can be distinguished and mapped from satellite sensor data. It is suggested that input of data on the position and extent of specifically wet snow and exposed glacial ice, which can be directly derived from remote sensing, should improve hydrological simulation of such basins.     

Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery

Leaf area index (LAI) is an important surface biophysical parameter as an input to many process-oriented ecosystem models. Much work has been reported in the literature on LAI estimation in boreal forests using remotely sensed imagery. However, few if any explicit LAI retrieval studies on bamboo forests in Asian subtropical monsoon-climate regions based on remote sensing technology have been performed. Our goal is to carry out a comparative study on the LAI estimation methods of bamboo forest in Fujian province, China, based on IRS P6 LISS 3 imagery. Both the traditional empirical–statistical approach and the newly proposed normalized distance (ND) method were employed in this study, and a total of 18 modelling parameters were regressed against ground-based LAI measurements. The results show that simple ratio (SR) is the best predictor for LAI estimation in this study area, with the highest R ² (coefficient of determination) value of 0.68; modified simple ratio (MSR) and normalized difference vegetation index (NDVI) ranked second and third, respectively. The good performance of these three vegetation indices (VIs) can be explained by the ratioing principle. The overall good modelling performance of the ND method in our study area also indicates it is a promising method.     

Role of geological and geomorphological factors in ground water exploration: a study using IRS LISS data

Various geological and geomorphical factors play a major role at different levels in the occurrence and movement of ground water in any terrain, especially in hard rock crystalline formations. In the present study three different test sites in the form of drainage basins representing diverse geological set ups in parts of Karnataka, India have been chosen to compare and evaluate the various factors that govern the ground water occurrence and also to assess the utility of Indian Remote Sensing Satellite (IRS) sensor data in ground water mapping and condition assessment. Selected digitally enhanced products were generated and utilized for extraction of relevant details on lithology, structure and landforms by their distinct image characteristics. The integration of the details extracted from digitally enhanced products along with morphometric parameters derived from the drainage maps, helped in the assessment of ground water conditions in each basin. The comparative assessment of the ground water controlling factors of all the three drainage basins has shown that, though similar types of hydrogeomor-phic landforms have developed over these drainage basins, they vary in terms of their characteristic behaviour and spatial distribution. The results obtained encourage the use of IRS sensor data for ground water targeting especially in hard rock terrain, where it is more complex and difficult.     

Applications of image texture in forest classification

Texture is an important property of the images. Its inclusion in digital classification is known to improve the classification accuracy. In the present study, the texture features angular second moment, entropy and inverse difference moment were used to differentiate and classify forests affected by jhum (shifting cultivation) in north-eastern India. Large increases (11·1 per cent) in the classification accuracy were observed when texture and tone were used simultaneously. In general, the inverse difference moment was found to be more useful than the entropy. The angular second moment was not useful. The most accurate classification was achieved with a combination of the tone, the entropy and the inverse difference moment.     

Comparison of support vector machine,artificial neural network,and spectral angle mapper algorithms for crop classification using LISS IV data

The Resourcesat-2 is a highly suitable satellite for crop classification studies with its improved features and capabilities. Data from one of its sensors, the linear imaging and self-scanning (LISS IV), which has a spatial resolution of 5.8 m, was used to compare the relative accuracies achieved by support vector machine (SVM), artificial neural network (ANN), and spectral angle mapper (SAM) algorithms for the classification of various crops and non-crop covering a part of Varanasi district, Uttar Pradesh, India. The separability analysis was performed using a transformed divergence (TD) method between categories to assess the quality of training samples. The outcome of the present study indicates better performance of SVM and ANN algorithms in comparison to SAM for the classification using LISS IV sensor data. The overall accuracies obtained by SVM and ANN were 93.45% and 92.32%, respectively, whereas the lower accuracy of 74.99% was achieved using the SAM algorithm through error matrix analysis. Results derived from SVM, ANN, and SAM classification algorithms were validated with the ground truth information acquired by the field visit on the same day of satellite data acquisition.     

Requirements for labelling forest polygons in an object-based image analysis classification

The ability to spatially quantify changes in the landscape and create land-cover maps is one of the most powerful uses of remote sensing. Recent advances in object-based image analysis (OBIA) have also improved classification techniques for developing land-cover maps. However, when using an OBIA technique, collecting ground data to label reference units may not be straightforward, since these segments generally contain a variable number of pixels as well as a variety of pixel values, which may reflect variation in land-cover composition. Accurate classification of reference units can be particularly difficult in forested land-cover types, since these classes can be quite variable on the ground. This study evaluates how many prism sample locations are needed to attain an acceptable level of accuracy within forested reference units in southeastern New Hampshire (NH). Typical forest inventory guidelines suggest at least 10 prism samples per stand, depending on the stand area and stand type. However, because OBIA segments group pixels based on the variance of the pixels, fewer prism samples may be necessary in a segment to properly estimate the stand composition. A bootstrapping statistical technique was used to find the necessary number of prism samples to limit the variance associated with estimating the species composition of a segment. Allowing for the lowest acceptable variance, a maximum of only six prism samples was necessary to label forested reference units. All polygons needed at least two prism samples for classification.     

用于数字图像传感器的脉冲宽度调制读出方法研究

为提高数字像素图像传感器的动态范围,提出了一种具有自适应参考电压的脉冲宽度调制读出方法。该方法将像素阵列分成包含相同数目像素的像素块,通过参考电压产生模块使每个像素块的参考电压和像素块内光照强度相关,理论上这种结构能够将数字像素图像传感器的动态范围从48 dB提升至96 dB,实际仿真结果为88.16 dB。分析了像素分块内主要的噪声来源和参考电压产生模块的采样电容引入的偏差。采用65 nm CMOS工艺实现了4×4的像素块电路,在高光强和弱光强条件下分别将电路输出同理论计算值相比较,并分析了产生误差的原因。     

Study of digital image processing techniques for leaf disease detection and classification

Multimedia Tools and Applications - In this paper, we address a comprehensive study on disease recognition and classification of plant leafs using image processing methods. The traditional manual...     

森林病虫害监测中的无人机图像分割算法比较

以错分率、相对最终测量精度以及运行时间为评价标准,利用无人机采集的油松及沙棘正射图像为测试图像,对6种基于像素聚类及分水岭的图像分割算法的性能进行了定性分析及定量比较。实验结果表明,受灾林区图像的分割算法的性能与图像拍摄高度、噪声等因素密切相关。最后,给出了受灾林区无人机正射图像分割算法应用的指导性建议。     

A survey of automated data augmentation algorithms for deep learning-based image classification tasks

In recent years, one of the most popular techniques in the computer vision community has been the deep learning technique. As a data-driven technique, deep model requires enormous amounts of accurately labelled training data, which is often inaccessible in many real-world applications. A data-space solution is Data Augmentation (DA), that can artificially generate new images out of original samples. Image augmentation strategies can vary by dataset, as different data types might require different augmentations to facilitate model training. However, the design of DA policies has been largely decided by the human experts with domain knowledge, which is considered to be highly subjective and error-prone. To mitigate such problem, a novel direction is to automatically learn the image augmentation policies from the given dataset using Automated Data Augmentation (AutoDA) techniques. The goal of AutoDA models is to find the optimal DA policies that can maximize the model performance gains. This survey discusses the underlying reasons of the emergence of AutoDA technology from the perspective of image classification. We identify three key components of a standard AutoDA model: a search space, a search algorithm and an evaluation function. Based on their architecture, we provide a systematic taxonomy of existing image AutoDA approaches. This paper presents the major works in AutoDA field, discussing their pros and cons, and proposing several potential directions for future improvements.

     

Performance analysis of image processing algorithms for classification of natural vegetation in the mountains of Southern California

Abstract

The Earth's forests fix carbon from the atmosphere during photosynthesis. Scientists are concerned that massive forest removals may promote an increase in atmospheric carbon dioxide, with possible global warming and related environmental effects. Space-based remote sensing may enable the production of accurate world forest maps needed to examine this concern objectively. To test the limits of remote sensing for large-area forest mapping, we use LANDSAT data acquired over a site in the forested mountains of southern California to examine the relative capacities of a variety of popular image processing algorithms to discriminate different forest types. Results indicate that certain algorithms are best suited to forest classification. Differences in performance between the algorithms tested appear related to variations in their sensitivities to spectral variations caused by background reflectance, differential illumination, and spatial pattern by species. Results emphasize the complexity between the land-cover regime, remotely sensed data and the algorithms used to process these data.     

Classification of impervious land-use features using object-based image analysis and data fusion

The proportion of impervious area within a watershed is a key indicator of the impacts of urbanization on water quality and stream health. Research has shown that object-based image analysis (OBIA) techniques are more effective for urban land-cover classification than pixel-based classifiers and are better suited to the increased complexity of high-resolution imagery. Focusing on five 2-km² study areas within the Black Creek sub-watershed of the Humber River, this research uses eCognition® software to develop a rule-based OBIA workflow for semi-automatic classification of impervious land-use features (e.g., roads, buildings, Parking Lots, driveways). The overall classification accuracy ranges from 88.7 to 94.3%, indicating the effectiveness of using an OBIA approach and developing a sequential system for data fusion and automated impervious feature extraction. Similar accuracy results between the calibrating and validating sites demonstrates the strong potential for the transferability of the rule-set from pilot study sites to a larger area.     

Object-aware image thumbnailing using image classification and enhanced detection of ROI

Thumbnail images are used to display a large collection of photos in various digital devices. It aims for people to browse and search the image collection effectively. The provided thumbnail images are expressed in a much lower resolution compared to the resolution of the original image. Thus, it faces a significant problem of how to represent the content of a given image effectively in a tiny thumbnail image. Many image thumbnailing methods have been presented in literature for this purpose. However, the existing thumbnailing methods are designed to use a single method to all kinds of images, regardless of image contents. On the other hand, the proposed method employs two different thumbnail generation methods either of which is applied according to corresponding image context. To achieve this, we first classify images into two groups by detecting the object existence. Then, an ROI cropping method using a saliency map is presented for images with objects, in order to represent the important region of images in the thumbnail. Images without any interesting objects, such as landscape images, are considered to be resized by using a simple scaling method to maintain the whole image context. Experimental results show that the proposed method yields comparable performance on a variety of datasets.     

New and efficient blind detection algorithm for digital image forgery using homomorphic image processing

Multimedia Tools and Applications - Digital image forgery detection is an important task in digital life as the image may be easily manipulated. This paper presents a novel blind tampering...     

Ensemble of feature sets and classification algorithms for sentiment classification

In this paper, we make a comparative study of the effectiveness of ensemble technique for sentiment classification. The ensemble framework is applied to sentiment classification tasks, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more accurate classification procedure. First, two types of feature sets are designed for sentiment classification, namely the part-of-speech based feature sets and the word-relation based feature sets. Second, three well-known text classification algorithms, namely na?¨ve Bayes, maximum entropy and support vector machines, are employed as base-classifiers for each of the feature sets. Third, three types of ensemble methods, namely the fixed combination, weighted combination and meta-classifier combination, are evaluated for three ensemble strategies. A wide range of comparative experiments are conducted on five widely-used datasets in sentiment classification. Finally, some in-depth discussion is presented and conclusions are drawn about the effectiveness of ensemble technique for sentiment classification.     

A comparison of machine and deep-learning algorithms applied to multisource data for a subtropical forest area classification

ABSTRACT

This work explores the integration of airborne Light Detection and Ranging (LiDAR) data and WorldView-2 (WV2) images to classify the land cover of a subtropical forest area in Southern Brazil. Different deep and machine learning methods were used: one based on convolutional neural network (CNN) and three ensemble methods. We adopted both pixel- (in the case of CNN) and object-based approaches. The results demonstrated that the integration of LiDAR and WV2 data led to a significant increase (7% to 16%) in accuracies for all classifiers, with kappa coefficient (κ) ranging from 0.74 for the random forest (RF) classifier associated with the WV2 dataset, to 0.92 for the forest by penalizing attributes (FPA) with the full (LiDAR + WV2) dataset. Using the WV2 dataset solely, the best κ was 0.81 with CNN classifier, while for the LiDAR dataset, the best κ was 0.8 with the rotation forest (RotF) algorithm. The use of LiDAR data was especially useful for the discrimination of vegetation classes because of the different height properties among them. In its turn, the WV2 data provided better performance for classes with less structure variation, such as field and bare soil. All the classification algorithms had a nearly similar performance: the results vary slightly according to the dataset used and none of the methods achieved the best accuracy for all classes. It was noticed that both datasets (WV2 and LiDAR) even when applied alone achieved good results with deep and machine learning methods. However, the advantages of integrating active and passive sensors were evident. All these methods provided promising results for land cover classification experiments of the study area in this work.     

Intrinsic evolvable hardware platform for digital circuit design and repair using genetic algorithms

A hardware/software platform for intrinsic evolvable hardware is designed and evaluated for digital circuit design and repair on Xilinx Field Programmable Gate Arrays (FPGAs). Dynamic bitstream compilation for mutation and crossover operators is achieved by directly manipulating the bitstream using a layered framework. Experimental results on a case study have shown that benchmark circuit evolution from an unseeded initial population, as well as a complete recovery of a stuck-at fault is achievable using this platform. An average of 0.47 μs is required to perform the genetic mutation, 4.2 μs to perform the single point conventional crossover, 3.1 μs to perform Partial Match Crossover (PMX) as well as Order Crossover (OX), 2.8 μs to perform Cycle Crossover (CX), and 1.1 ms for one input pattern intrinsic evaluation. These represent a performance advantage of three orders of magnitude over the JBITS software framework and more than seven orders of magnitude over the Xilinx design tool driven flow for realizing intrinsic genetic operators on Xilinx Virtex Family devices.     

Evaluation of supervised classification algorithms for identifying crops using airborne hyperspectral data

Sufficient training data must be acquired to classify areas of interest using a supervised classification method and hyperspectral data. However, the relatively small size of agricultural plots in Japan means that there is no training area large enough to represent a feature of interest. In this study, a new method for identifying crops using hyperspectral remotely sensed data has been proposed in order to resolve the problem of identifying training areas in agricultural crops. This method was then compared with conventional methods. The proposed method was found to be most effective for identifying crops using hyperspectral data in an agricultural land area.     

Detection of cracks using image processing algorithms implemented in hardware

Cracks originate in a multitude of industrial artefacts and their detection is of considerable social and economic importance. The article discusses various image processing algorithms for recognizing cracks and their implementation in fast electronic hardware. The authors maintain that this inspection task is so important that it would be well worthwhile building equipment specifically for detecting cracks. With suitable optics, it should be applicable to a wide range of industries.     

Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium

Obtaining detailed information about the amount of forest cover is an important issue for governmental policy and forest management. This paper presents a new approach to update the Flemish Forest Map using IKONOS imagery. The proposed method is a three-step object-oriented classification routine that involves the integration of 1) image segmentation, 2) feature selection by Genetic Algorithms (GAs) and 3) joint Neural Network (NN) based object-classification. The added value of feature selection and neural network combination is investigated. Results show that, with GA-feature selection, the mean classification accuracy (in terms of Kappa Index of Agreement) is significantly higher (p < 0.01) than without feature selection. On average, the summed output of 50 networks provided a significantly higher (p < 0.01) classification accuracy than the mean output of 50 individual networks. Finally, the proposed classification routine yields a significantly higher (p < 0.01) classification accuracy as compared with a strategy without feature selection and joint network output. In addition, the proposed method showed its potential when few training data were available.