Feature Selection in AVHRR Ocean Satellite Images by Means of Filter Methods

Piedra-Fernández, José A.; Cantón-Garbín, M.; Wang, James

doi:10.1109/tgrs.2010.2050067

Cited by 19 publications

(7 citation statements)

References 41 publications

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“…Furthermore, the correlation-based feature selection (CFS) [81] with the best first search algorithm [82] was used to further select the most effective features for this study [83]. CFS has been reported to be an effective tool to select the optimal feature subset from complex remote sensing datasets [84]. As a classic filter feature selection mode, CFS calculates the feature-class and feature-feature correlation matrices based on the training set, and then search the feature subset using the best first search algorithm based on the redundancy between features.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

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Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi'ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification.

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Section: Feature Extraction and Selectionmentioning

confidence: 99%

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

et al. 2018

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“…give morphologic descriptions of an object as well as its location. Nevertheless, they do not give enough contextual information, and more importantly, they do not deal with spectral information (temperature and chlorophyll levels compared to the surroundings), which is crucial for MOS identification [17]. The expert knowledge of oceanographers is therefore required.…”

Section: Sea-viewing Field-of-view Sensor (Seawifs) Of Chlorophyll-a mentioning

confidence: 99%

OBIA System for Identifying Mesoscale Oceanic Structures in SeaWiFS and MODIS-Aqua Images

Vidal-Fernandez

Piedra-Fernández

Almendros-Jiménez

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-The ocean covers over 70% of the surface of our planet and plays a key role in the global climate. Most ocean circulation is mesoscale (scales of 50-500 km and 10-100 days), and the energy in mesoscale circulation is at least one order of magnitude greater than general circulation; therefore, the study of mesoscale oceanic structures (MOS) is crucial to ocean dynamics, making it especially useful for analyzing global changes. The detection of MOS, such as upwellings or eddies, from satellites images is significant for marine environmental studies and coastal resource management. In this paper, we present an objectbased image analysis (OBIA) system which segments and classifies regions contained in sea-viewing field-of-view sensor (SeaWiFS) and Moderate Resolution Imaging Spectro-radiometer (MODIS)-Aqua sensor satellite images into MOS. After color clustering and hierarchical data format (HDF) file processing, the OBIA system segments images and extracts image descriptors, producing primary regions. Then, it merges regions, recalculating image descriptors for MOS identification and definition. First, regions are labeled by a human-expert, who identifies MOS: upwellings, eddies, cool, and warm eddies. Labeled regions are then classified by learning algorithms (i.e., decision tree, Bayesian network, artificial neural network, genetic algorithm, and near neighbor algorithm) from selected features. Finally, the OBIA system enables images to be queried from the user interface and retrieved by means of fuzzy descriptors and oceanic structures. We tested our system with images from the Canary Islands and the North West African coast.Index Terms-Automatic recognition, fuzzy logic, image retrieval, moderate resolution imaging spectro-radiometer (MODIS), object-based image analysis (OBIA), ocean satellite images, sea-viewing field-of-view sensor (SeaWiFS).

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“…Our research group has a wide experience in the development of methods and techniques for the analysis of ocean satellite images, mainly focused on sensors capturing the area of study (i.e., Canary Islands and North West African coast) (see [28][29][30][31][32], for the most recent contributions). In particular, the same dataset (and thus of the same sensor) was used in two previous works [28,29].…”

Section: Related Workmentioning

confidence: 99%

Extraction of Knowledge Rules for the Retrieval of Mesoscale Oceanic Structures in Ocean Satellite Images

Vidal-Fernandez

Almendros-Jiménez

Piedra

et al. 2016

Hybrid Soft Computing for Image Segmentation

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The processing of ocean satellite images has as goal the detection of phenomena related with ocean dynamics. In this context, Mesoscale Oceanic Structures (MOS) play an essential role. In this chapter we will present the tool developed in our group in order to extract knowledge rules for the retrieval of MOS in ocean satellite images. We will describe the implementation of the tool: the workflow associated with the tool, the user interface, the class structure, and the database of the tool. Additionally, the experimental results obtained with the tool in terms of fuzzy knowledge rules as well as labeled structures with these rules are shown. These results have been obtained with the tool analyzing chlorophyll and temperature images of the Canary Islands and North West African coast captured by the SeaWiFS and MODIS-Aqua sensors.

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Feature Selection in AVHRR Ocean Satellite Images by Means of Filter Methods

Cited by 19 publications

References 41 publications

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models

OBIA System for Identifying Mesoscale Oceanic Structures in SeaWiFS and MODIS-Aqua Images

Extraction of Knowledge Rules for the Retrieval of Mesoscale Oceanic Structures in Ocean Satellite Images

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