**Abstract**

Image processing is growing fast and persistently. The idea of remotely sensed image clustering is to categorize the image into meaningful land use land cover classes with respect to a particular application. Image clustering is a technique to group an image into units or categories that are homogeneous with respect to one or more characteristics. There are many algorithms and techniques that have been developed to solve image clustering problems, though, none of the method is a general solution. This chapter will highlight the various clustering techniques that bring together the current development on clustering and explores the potentiality of those techniques in extracting earth surface features information from high spatial resolution remotely sensed imageries. It also will provide an insight about the existing mathematical methods and its application to image clustering. Special emphasis will be given on Hölder exponent (HE) and Variance (VAR). HE and VAR are well-established techniques for texture analysis. This chapter will highlight about the Hölder exponent and variance-based clustering method for classifying land use/land cover in high spatial resolution remotely sensed images.

**Keywords:** remote sensing image, clustering, classification, land use, land cover, features, extraction

## **1. Introduction**

High spatial resolution remotely sensed imagery helps to obtain quality and detailed information about the earth's surface features in conjunction with their geographical associations. The internal changeability within the identical land-use land-cover units augments with the rise of resolution. The augmented changeability diminishes the statistical distinguishability of land-use/land-cover classes in the spectral data space. This reduced distinguishability tends to decrease the accuracies of pixel-based clustering algorithms such as Fuzzy C Means [1], minimum distance classifiers [2] and K-Means [3]. These pixel-based clustering techniques assign a pixel to a region according to the similarities of spectral signature. It considers only one pixel at a time [4]. Spectral signatures are the specific combination of emitted, reflected or absorbed electromagnetic (EM) radiation at varying wavelengths which can uniquely identify an object [4].

Compared to IRS-1A/1B sensors, the spectral resolution of high spatial resolution images is normally relatively poor. Spectral resolution describes a sensor's ability to

identify fine intervals of wavelength. The better the spectral resolution, the finer the channel or band width. Therefore, between spatial and spectral resolution, there is a trade-off. It is mainly true for panchromatic (PAN) images of high spatial resolution, namely CARTOSAT-II 1m and IKONOS 1m. There is a need to consider the spatial relationships between pixel values, also known as the 'texture' of the scene objects to classify high-resolution (HR) images owing to the wide difference in the spatial structure in these images. Consequently, multiple texture-based clustering technique namely GLCM [5–8], Markov random field (MRF) model [5], Gray scale rotation invariant [9] were evolved for clustering remote sensing images having high spatial resolution. Nevertheless, above mentioned methods are appropriate in textured area of HR images. A region is called textured; where the intensity dissimilarity within adjacent pixels is substantial. A region is said to be nontextured, where the intensity dissimilarity among adjacent pixels is insignificant [10, 11]. But texture-based classification techniques failed in non-textured region of high spatial resolution image as much variation is not found in the spatial pattern of those regions of the image [12]. Thus, we can infer from earlier studies that classification of high spatial resolution imageries either by pixel or texture-based algorithm may not yield desired results.

image is segmented and non-textured and textured regions are extracted from the

*Clustering Techniques for Land Use Land Cover Classification of Remotely Sensed Images*

The Hölder Exponent (HE) and VAR are jointly used to convert the image for computing the texture. The HE calculates each pixel of P's spatial structure. Besides spatial structure, local image contrast also grasps important property for computing the texture around the pixel. In this research, therefore, VAR is used to calculate the

Hölder exponent has been used for investigating the texture in high-resolution images [12]. It measures the irregularity in the vicinity. Supremacy of applying Hölder Exponent in HR images are that (i) it can be used as an instrument to calculate each pixel of the image's spatial structure, (ii) no previous data on the

**Definition of HE** [27]: Let *μ* be a measure on a set Ω as well as for all *x* Є Ω,

A sequence of 15 values of radius r (i.e. 1, √2, √5, 3, √13, 3√2, 5, √29, 2√10,

3√5, 7, √61, 6√2, √85, 7√2) centered on *x* are used as a scale parameter for calculating HE value around each pixel *x* in the image [12] and the total number (*N*) of intersected pixels by the perimeter of series of circles of radius *r* is considered as a scale parameter for computing VAR value around *x* [12]. *N* is computed using Eq. (1).

> *<sup>N</sup>* <sup>¼</sup> <sup>X</sup>*<sup>t</sup> r*¼1

where t is the total number of identified circles, *mr* is the number of intersected

*) for contrast measurement around each pixel of the image*

To get the contrast value of (x, y), the neighbor's σ<sup>2</sup> of each pixel (x, y) is

P*<sup>t</sup> r*¼1 P*mr*

P*<sup>t</sup> r*¼1 P*mr <sup>j</sup>*¼<sup>1</sup>*arj*

*N*

Thus obtained *α*(*x*,*y*) and σ<sup>2</sup> (x,y) for each P(x,y). Afterward, these values are used in Eq. (3) to obtain the corresponding pixel value (x,y) in the transformed image T. Each pixel (x,y) of T signifies the degree of texture around that pixel.

*T x*ð Þ¼ , *<sup>y</sup> <sup>α</sup>*ð Þþ *<sup>x</sup>*, *<sup>y</sup>* <sup>σ</sup><sup>2</sup> x, y � �

*<sup>j</sup>*¼<sup>1</sup> *arj* � <sup>μ</sup> � �<sup>2</sup>

calculated over the entire image. Using Eq. (2), the σ<sup>2</sup> (x, y) is realized

μ ¼

ð Þ¼ *x*, *y*

σ2

where *arj* is the intensity value of pixel (*r,j*),

, for small *r*. Here B*r*(*x*) is circle (2D) of radius *i*

*mr* (1)

*<sup>N</sup>* (2)

<sup>2</sup> (3)

initial image. Finally, the two areas obtained are separately clustered.

pixel intensity is required and (iii) is not very sensitive to noise [12].

*α*

centered on *x*. Then *α* (*x*) is called the HE on *x.*

pixels on the perimeter of the radius r circle.

**2.1 Transformation of image**

*DOI: http://dx.doi.org/10.5772/intechopen.89165*

contrast around the pixel.

э α(*x*), such that *μ* (*Br*(*x*)) � *r*

*2.1.1 Hölder exponent*

*2.1.2 VAR (σ<sup>2</sup>*

**109**

Some more techniques namely watershed approach [13, 14], region-growing approach [4, 15], mean shift approach [16, 17], region merging approach [18] etc. are in use for clustering high spatial resolution remote sensing images. Application of these approaches for clustering of images either leads to under-segmentation or over-segmentation [19, 20]. Structural image indexing approach [21], semisupervised feature learning approach [22] and multi-scale manner using SVM approach [23] are also found fairly suitable in clustering high resolution images. The imagery of higher resolution includes textured and non-textured areas. Hence, pixel or texture-based algorithm for clustering of high-resolution imagery does not produce expected results. This type of high-resolution imagery clustering research is in the trend. Multi-circular local binary pattern and variance-based method [10] were used separately to cluster high resolution image having textured and non-textured regions. The Multi circular local binary pattern operator has been used here for measuring the spatial structure of the image. But, disadvantage in this strategy is that multi-circular local binary pattern operator is susceptible to noise as it exactly sees the value of the moving window's central pixel as a limit for computing the spatial structure around the central pixel.

In last one decade the Hölder exponent (HE) has been used for calculating spatial structure of the images [24–26]. It is also being used for clustering highresolution images [12]. HE gives an evidence of the spatial structure of the image and is not much influenced by the noise. In addition, spatial structure, contrast of the local image holds considerable property for calculating the texture around the pixel**.** In this research, high-resolution picture textured and non-textured region is originally segmented using HE and VAR-based method and subsequently separately clustered and non-textured areas. VAR is used to calculate the contrast around the pixel. The suggested method is applied with a 1 m spatial resolution on high resolution IKONOS PAN images.
