A cognitive pyramid for contextual classification of remote sensing images

Binaghi, Elisabetta; Gallo, Ignazio; Pepe, Monica

doi:10.1109/tgrs.2003.815409

Cited by 49 publications

(30 citation statements)

References 21 publications

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“…image pyramid, wavelet transform and hierarchical image partitions. Binaghi et al analyzed high-resolution scenes through a set of concentric windows and a Gaussian pyramidal resampling approach (Binaghi, Gallo, and Pepe 2003). Yang and Newsam (2011) proposed a spatial pyramid co-occurrence to characterize the photometric and geometric aspects of images.…”

Section: Related Workmentioning

confidence: 99%

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Wang

Bai

et al. 2018

Geo-spatial Information Science

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c state Key laboratory for information engineering in surveying, mapping and remote sensing (liesmars), Wuhan University, Wuhan, china ABSTRACT Unmanned aerial vehicle (UAV)-based imaging systems have many superiorities compared with other platforms, such as high flexibility and low cost in collecting images, providing wide application prospects. However, the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images, which poses great challenges for subsequent applications. Therefore, an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time. In this work, we proposed a multi-scale hierarchical representation, i.e. binary partition tree, for analyzing large-scale UAV images. More precisely, we first obtained an initial partition of images by an oversegmentation algorithm, i.e. the simple linear iterative clustering. Next, we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships. Moreover, objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure. Experimental results on processing the post-seismic UAV images of the 2013 Ya'an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.

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Section: Related Workmentioning

confidence: 99%

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Wang

Bai

et al. 2018

Geo-spatial Information Science

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“…Therefore it is very important to consider the object differences at different scales. Several studies have utilized Gaussian pyramid image decomposition to build a hierarchical image representation [6,20]. In [6], Binaghi et al analyzed a high-resolution…”

Section: Related Workmentioning

confidence: 99%

“…In addition, given the multi-scale cognitive mechanism underlying the human visual system, which operates on the level of the object to the environment and then to the background, analysis on a single scale is insufficient for extracting all semantic objects. To represent HRS images on multiple scales, three main methods are utilized: image pyramid [6], wavelet transform [7] and hierarchical image partitions [8]. However, how to consider the intrinsic properties of local objects in multi-scale image representation is a key problem worth studying.…”

Section: Introductionmentioning

confidence: 99%

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

Yang

Xia

et al. 2016

Remote Sensing

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Scene classification plays an important role in understanding high-resolution satellite (HRS) remotely sensed imagery. For remotely sensed scenes, both color information and texture information provide the discriminative ability in classification tasks. In recent years, substantial performance gains in HRS image classification have been reported in the literature. One branch of research combines multiple complementary features based on various aspects such as texture, color and structure. Two methods are commonly used to combine these features: early fusion and late fusion. In this paper, we propose combining the two methods under a tree of regions and present a new descriptor to encode color, texture and structure features using a hierarchical structure-Color Binary Partition Tree (CBPT), which we call the CTS descriptor. Specifically, we first build the hierarchical representation of HRS imagery using the CBPT. Then we quantize the texture and color features of dense regions. Next, we analyze and extract the co-occurrence patterns of regions based on the hierarchical structure. Finally, we encode local descriptors to obtain the final CTS descriptor and test its discriminative capability using object categorization and scene classification with HRS images. The proposed descriptor contains the spectral, textural and structural information of the HRS imagery and is also robust to changes in illuminant color, scale, orientation and contrast. The experimental results demonstrate that the proposed CTS descriptor achieves competitive classification results compared with state-of-the-art algorithms.

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“…Recently, advancing research is incorporating contextual information on relationships between the target object and its surrounding environment [20]. This relationship information, derived from pixels or objects in remote sensing imagery, is called spatial contextual information [21,22]. Spatial contextual information can be used in various data sets-including multispectral imagery [23], synthetic aperture radar (SAR) [24], and LiDAR data [25]-as well as for different extraction purposes, such as forest fire mapping [26], cloud detection [27], and building detection [28].…”

Section: Introductionmentioning

confidence: 99%

Rural Settlement Subdivision by Using Landscape Metrics as Spatial Contextual Information

Zheng

Weston

et al. 2017

Remote Sensing

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Multiple policy projects have changed land use and land cover (LULC) in China's rural regions over the past years, resulting in two types of rural settlements: new-fashioned and old-fashioned. Precise extraction of and discrimination between these two settlement types are vital for sustainable land use development. It is difficult to identify these two types via remote sensing images due to their similarities in spectrum, texture, and geometry. This study attempts to discriminate different types of rural settlements by using a spatial contextual information extraction method based on Gaofen 2 (GF-2) images, which integrate hierarchical multi-scale segmentation and landscape analysis. A preliminary LULC map was derived by using only traditional spectral and geometrical features from a finer scale. Subsequently, a vertical connection was built between superobjects and subobjects, and landscape metrics were computed. The vertical connection was used for assigning landscape contextual information to subobjects. Finally, a classification phase was conducted, in which only multi-scale contextual information was adopted, to discriminate between new-fashioned and old-fashioned rural settlements. Compared with previous studies on multi-scale contextual information, this paper employs landscape metrics to quantify contextual characteristics, rather than traditional spectral, textural, and topological relationship information, from superobjects. Our findings indicate that this approach effectively identified and discriminated two types of rural settlements, with accuracies over 80% for both producers and users. A comparison with a conventional top-down hierarchical classification scheme showed that this novel approach improved accuracy, precision, and recall. Our results confirm that multi-scale contextual information with landscape metrics provides valuable spatial information for classification, and indicates the practicability, applicability, and effectiveness of this synthesized approach in distinguishing different types of rural settlements.

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A cognitive pyramid for contextual classification of remote sensing images

Cited by 49 publications

References 21 publications

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Analysis of large-scale UAV images using a multi-scale hierarchical representation

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

Rural Settlement Subdivision by Using Landscape Metrics as Spatial Contextual Information

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