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In this paper, we present a new method to improve the performance of current bag-of-word based image classification process. After feature extraction, we introduce a pair wise image matching scheme to select the discriminative features. Only the label information from the raining-sets is used to update the feature weights via an iterative matching processing. The selected features correspond to the...
This paper proposes a procedure aimed at efficiently adapting a classifier trained on a source image to a similar target image. The adaptation is carried out through active queries in the target domain following a strategy particularly designed for the case where class distributions have shifted between the two images. We first suggest a pre-selection of candidate pixels issued from the target image...
Many studies [1]–[2] show that classification techniques with both spectral and spatial information are effective to overcome the similar spectral properties in hyperspectral image classification problem. Moreover, kernel-based methods have attracted much attention in the area of pattern recognition and machine learning, many researches [3]–[5] show that kernel method is computationally efficient,...
The Human Visual System (HVS) has been observed to process visual information on a multi-channel filtering basis in the early stages of analysis. This has given rise to a number of texture segmentation techniques that seek to mimic the HVS multi-channel filtering theory.
In this paper, a semi-supervised technique based on support vector machine (SVM) for image classification and a Locality Sensitive Hashing (LSH) based searching algorithm to search for similarity of satellite imagery is presented. Given a query image, the goal is to retrieve matching images in the database based on the shape features extracted from satellite imagery data. The experimental results...
Land cover classification accuracy assessments are frequently limited to an error matrix, which derived from location-independent measures and consequently doesn't provide any information about the spatial distribution of the error. The objective of this work is to present a methodology for mapping the spatial distribution of classification errors based on stochastic simulation and that takes into...
Supervised and unsupervised learning are two well disseminated and discussed paradigms which define how image classification techniques extract knowledge about the data. A recent learning paradigm, called semi-supervised, comes to solve some limitations of supervised learning, as the amount of information needed to conduce an appropriated learning process. Different models of semi-supervised learning...
The grassland in China occupies more than 40% of its rural land area. However, grassland degradation has been a serious problem in recent years. Thus, a policy of returning cultivated land into grassland is enacted. An object-oriented image classification using different feature objects was adopted to classify grassland and a hierarchy of layers in different years for change detection was deployed...
The objective of this paper is to report on the crop classification activities carried out during the first year of the Italian project “Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites” (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology...
Image texture features extracted from high-resolution remotely sensed images over urban areas have shown promise in their ability to distinguish different settlement classes. Without any explicit mechanism to counter the effects of variable illumination- and viewing geometries, these features may not generalize well in multi-date applications such as change detection. This paper presents the results...
High accurate airborne Light Detection and Range (LiDAR) is widely accepted as one kind of survey data sources. However, with the LiDAR products including Digital Elevation Model (DEM), Digital Surface Model (DSM) and intensity image, land use classifications and feature extractions were generally combined with optical images including satellite images or aerial photos using relative segmentations...
Band selection for hyperspectral images is an effective technique to mitigate the curse of dimensionality. A variety of band selection methods have been suggested in the past. This paper presents a novel band prioritization based on impurity function (IF) for the band selection of hyperspectral images. The proposed IF band selection (IFBS) is incorporated with particle swarm optimization (PSO) band...
The accuracy of rainforests classification is generally improved by the input of multisensory data since complex vegetation type identification benefits from complementary information. However, in some cases, multisource fusion can also deteriorate accuracy when irrelevant sources are added. Thus, we introduce a fusion method for classes “in difficulty”. Our method outperforms the classical global...
On the research of agricultural Non-point source pollution, agricultural land use data is the most important basic information, it's hard to obtain by statistical means and it update frequently. By the help of remote sensing image and information extraction techniques, agricultural land use information can be extracted with low-cost and fast and accurately. In this study, we established index system...
Hyperspectral imagery offers an effective way to automatically mapping of surface sediment types in the intertidal flat. The objective of this study is to determine the possibility of mapping sediment types in the southern Sheyang River mouth, Jiangsu Province from Hyperion satellite data with linear spectral unmixing method. Results indicate that the mapping was achieved at an overall accuracy of...
The performance of two supervised classifiers, linear and regularized discriminant analysis (LDA and RDA), is compared here for canopy species discrimination in humid tropical forest, based on airborne hyperspectral imagery acquired with the sensor Carnegie Airborne Observatory Alpha System (CAO-Alpha). Classification is performed to identify 13 species at pixel scale, crown scale, and using an object-based...
Considering the simplicity and fast training speed of Haar-like features, the high detecting precision of HOG features, a combined method is proposed on the basis of the two features. Several rectangular features which can describe local human characteristics based on original features are added. The combined method can retain the precision of HOG features and increase the speed of detection at the...
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