Search

In this article, we develop a novel feature extraction method that combines two well-established mathematical morphology concepts: watersheds and morphological attribute profiles (APs). In order to extract spatial-spectral features from remote sensing data, APs were originally defined as sequences of filtering operators on inclusion trees, i.e., the max- and min-trees, computed from the input image. In this study, we extend the AP paradigm to the more general framework of hierarchical watersheds. Moreover, we explore the semantic knowledge provided by labeled training pixels during different phases of the watershed-AP construction, namely within the construction of hierarchical watersheds from the raw image and later within the filtering of the resulting hierarchy. We illustrate the relevance of the proposed method with two applications including land cover classification and building extraction using optical remote sensing images. Experimental results show that the new profiles outperform various existing features using two public datasets (Zurich and Vaihingen), thus providing another high potential feature extraction method within the AP family.

Estimating reliably chlorophyll-a (Chl-a) concentration from remote sensing images constitutes a vastly superior alternative to field measurements. To this end, spectral pixel signatures are used commonly for developing regression models. Spatial information has been traditionally ignored in this context, as Chl-a concentration is a spatially localized measurement, and sensors’ spatial resolutions have been relatively low in the past. However, the increased spatial resolution of newer satellites and a recent study have given strong indications that spatial–spectral description can boost estimation performance. Consequently, in this letter, we address the problem of Chl-a estimation from remote sensing images using attribute profiles, one of the paramount spatial–spectral description tools. We further propose an original technique to remove their cumbersome threshold requirement via operating on each pixel’s “attribute lineage.” We validate our approach with multispectral Sentinel-2 images, and a data set formed by field measurements spanning almost two years over Lake Balik (Turkey). We show that the proposed method outperforms various alternatives in terms of regression performance, across multiple experimental setups, and finally, we highlight a validation malpractice encountered often in the field of water quality estimation. [ABSTRACT FROM AUTHOR]

Detailed land cover change in multitemporal images is an important application for earth science. Many techniques have been proposed to solve this problem in different ways. However, accurately identifying changes still remains a challenge due to the difficulties in describing the characteristics of various change categories by using single-level features. In this article, a multilevel feature representation framework was designed to build robust feature set for complex change detection task. First, four different levels of information from low level to high level, including pixel-level, neighborhood-level, object-level, and scene-level features, were extracted. Through the operation of extracting different level features from multitemporal images, the differences between them can be described comprehensively. Second, multilevel features were fused to reduce the dimension and then used as the input for supervised change detector with initial limited labels. Finally, for reducing the labeling cost and improving the change detection results simultaneously, active learning was conducted to select the most informative samples for labeling, and this step together with the previous steps were iteratively conducted to improve the results in each round. Experimental results of three pairs of real remote sensing datasets demonstrated that the proposed framework outperformed the other state-of-the-art methods in terms of accuracy. Moreover, the influences of scene scale for high-level semantic features in the proposed approach on change detection performance were also analyzed and discussed.

Attribute profiles are well-acknowledged as one of the most significant techniques to characterize spectral-spatial properties of a hyperspectral image. The spectral-spatial content of an attribute profile is influenced by the threshold values considered during its construction. In this article, we propose a robust method to detect the threshold values automatically by overcoming the limitations of the existing techniques. The proposed method employs a tree structure representing the connected components of the image and evaluates attribute values at each node. Then, a total characteristic function (TCF) is defined that represents these attribute values in a nondecreasing order. The defined TCF is analyzed using a novel technique to detect a few informative thresholds for the construction of a low-dimensional attribute profile representing substantial spectral-spatial information. The proposed threshold detection method is computationally efficient. To assess the effectiveness of the proposed technique experiments are conducted on three real hyperspectral datasets using six different attributes and the results are compared to the recent state-of-the-art method. The results demonstrate that the proposed method has several advantages over the existing state-of-the-art method.

Attribute profiles (APs) are among the most prominent spectral-spatial pixel description tools, widely employed with optical, and hyperspectral images in particular. Their use, however, with the SAR data has been very limited. In this letter, we investigate the use of the recently developed feature attribute profiles (FPs) for the pixel-based classification of SAR images. Compared to low-level features such as intensity and amplitude, FPs are shown to provide high-level features incorporating successfully the spatial information of the pixels. Based on the classification results obtained on real TerraSAR-X images, it is shown that the FPs are capable of more accurately classifying the pixels compared to conventional and widely used SAR feature extraction methods such as gray-level co-occurrence matrices, histograms of oriented gradients, local binary patterns, and Gabor filters. [ABSTRACT FROM AUTHOR]

This paper introduces an extension of morphological attribute profiles (APs) by extracting their local features. The so-called local feature-based APs (LFAPs) are expected to provide a better characterization of each APs' filtered pixel (i.e., APs' sample) within its neighborhood, and hence better deal with local texture information from the image content. In this paper, LFAPs are constructed by extracting some simple first-order statistical features of the local patch around each APs' sample such as mean, standard deviation, and range. Then, the final feature vector characterizing each image pixel is formed by combining all local features extracted from APs of that pixel. In addition, since the self-dual APs (SDAPs) have been proved to outperform the APs in recent years, a similar process will be applied to form the local feature-based SDAPs (LFSDAPs). In order to evaluate the effectiveness of LFAPs and LFSDAPs, supervised classification using both the random forest and the support vector machine classifiers is performed on the very high resolution Reykjavik image as well as the hyperspectral Pavia University data. Experimental results show that LFAPs (respectively, LFSDAPs) can considerably improve the classification accuracy of the standard APs (respectively, SDAPs) and the recently proposed histogram-based APs. [ABSTRACT FROM AUTHOR]

This paper proposes a novel extension of morphological attribute profiles (APs) for classification of remote sensing data. In standard AP-based approaches, an input image is characterized by a set of filtered images achieved from the sequential application of attribute filters based on the image tree representation. Hence, only pixel values (i.e. gray levels) are employed to form the output profiles. In this paper, during the attribute filtering process, instead of outputting the gray levels, we propose to extract both statistical and geometrical features from the connected components (w.r.t. tree nodes) to build the so-called feature profiles (FPs). These features are expected to better characterize the object or region encoded by each connected component. They are then exploited to classify remote sensing images. To evaluate the effectiveness of the proposed approach, supervised classification using the random forest classifier is conducted on the panchromatic Reykjavik image as well as the hyperspectral Pavia University data. Experimental results show the FPs provide a competitive performance compared against standard APs and thus constitute a promising alternative. [ABSTRACT FROM PUBLISHER]

Effective spatial–spectral pixel description is of crucial significance for the classification of hyperspectral remote sensing images. Attribute profiles are considered as one of the most prominent approaches in this regard, since they can capture efficiently arbitrary geometric and spectral properties. Lately though, the advent of deep learning in its various forms has also led to remarkable classification performances by operating directly on hyperspectral input. In this letter, we explore the collaboration potential of these two powerful feature extraction approaches. Specifically, we propose a new strategy for hyperspectral image classification, where attribute filtered images are stacked and provided as input to convolutional neural networks. Our experiments with two real hyperspectral remote sensing data sets show that the proposed strategy leads to a performance improvement, as opposed to using each of the involved approaches individually. [ABSTRACT FROM PUBLISHER]

Extended attribute profiles (EAPs) have been widely used for the classification of high-resolution hyperspectral images. EAPs are obtained by computing a sequence of attribute operators. Attribute filters (AFs) are connected operators, so they can modify an image by only merging its flat zones. These filters are effective when dealing with very high resolution images since they preserve the geometrical characteristics of the regions that are not removed from the image. However, AFs, being connected filters, suffer the problem of “leakage” (i.e., regions related to different structures in the image that happen to be connected by spurious links will be considered as a single object). Objects expected to disappear at a certain threshold remain present when they are connected with other objects in the image. The attributes of small objects will be mixed with their larger connected objects. In this paper, we propose a novel framework for morphological AFs with partial reconstruction and extend it to the classification of high-resolution hyperspectral images. The ultimate goal of the proposed framework is to be able to extract spatial features which better model the attributes of different objects in the remote sensed imagery, which enables better performances on classification. An important characteristic of the presented approach is that it is very robust to the ranges of rescaled principal components, as well as the selection of attribute values. Our experimental results, conducted using a variety of hyperspectral images, indicate that the proposed framework for AFs with partial reconstruction provides state-of-the-art classification results. Compared to the methods using only single EAP and stacking all EAPs computed by existing attribute opening and closing together, the proposed framework benefits significant improvements in overall classification accuracy. [ABSTRACT FROM PUBLISHER]

In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images. The hyperspectral data are reduced into a set of components by nonparametric weighted feature extraction (NWFE), and a morphological processing is then performed by the SDAPs separately on each of the extracted components. Since the spatial information extracted by SDAPs results in a high number of features, the NWFE is applied a second time in order to extract a fixed number of features, which are finally classified. The experiments are carried out on two hyperspectral images, and the support vector machines and random forest are used as classifiers. The effectiveness of SDAPs is assessed by comparing its results against those obtained by an approach based on extended APs. [ABSTRACT FROM PUBLISHER]

Morphological and attribute profiles have been proven to be effective tools to fuse spectral and spatial information for classification of remote sensing data. A wide range of filters (i.e., number of levels in the profiles) is usually necessary in order to properly model the spatial information in a remote sensing scene. A dense sampling of the values of the parameters of the filters generates profiles that have both a very large dimensionality (leading to the Hughes phenomenon in classification) and a high redundancy. In this paper, a novel iterative technique based on genetic algorithms (GAs) is proposed to automatically optimize the selection of the optimal features from the profiles. The selection of the filtered images that compose the profile is performed by dividing them into three classes corresponding to high, medium, and low importance. We propose to measure the importance (modeled in terms of discriminative power in the classification task) using a random forest classifier, which provides a rank for each feature with its model. Only the set of images associated with the highest importance is selected, i.e., preserved for classification. The proposed technique is applied to the features labeled with medium importance, whereas the images with the lowest importance are removed from the profile. This method is employed to classify three hyperspectral data sets achieving significantly high classification accuracy values. A parallel computing implementation has been developed in order to significantly reduce the time required for the run of the GAs. [ABSTRACT FROM AUTHOR]

Extended attribute profiles, which are based on attribute filters, have recently been presented as efficient tools for spectral-spatial classification of remote sensing images. However, construction of these profiles usually requires manual selection of parameters for the corresponding attribute filters. In this letter, we present a technique to automatically build the extended attribute profiles with the standard deviation attribute based on the statistics of the samples belonging to the classes of interest. The methodology is tested on two widely used hyperspectral images and the results are found to be highly accurate. [ABSTRACT FROM PUBLISHER]