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Improving segmentation accuracy for detecting deforestation using texture feature derived from Landsat 8 OLI multispectral imagery

Abstract For the identification of deforestation, numerous techniques and features have been developed and used to maximize the segmentation accuracy. Choosing appropriate texture features and extraction methods are one of the most significant issues in remote sensing image applications. This work proposes a Modified Sum of Difference using diagonal texture feature extraction with multiresolution segmentation algorithm to maximize the segmentation accuracy. First, the Modified Sum of Difference using diagonal matrix (MSODDM) algorithm is applied to the input image; this divides the image into a number of coarse objects. Secondly, the multiresolution algorithm with the threshold value of 200 is applied to the coarse image produced by MSODDM for getting finer segmentation result. Finally, the accuracy of the segmented image is compared with the sample input image used in the experimental study. Thus, the proposed method generates more promising result of reaching 97.02% of segmentation accuracy, which results in better classification of remote sensing images in detecting deforestation.


Introduction
Remote sensing can provide prompt and accurate information on earth observations and also give valuable and resourceful information of deforestation for decisionmakers of forest fire management system and forest analysts to take essential actions as it provides more information especially in high altitude problem areas such as of landscape change, monitoring forest degradation and deforestation, sea water level calculations, etc [Fu et al., 2013]. Hence the paper focuses on improving the remote sensing image segmentation using texture feature extraction algorithm for analyzing 170 deforestation. The texture of an area can describe the local spatial organisation of spatially changeable spectral values [Srinivasan and Shobha, 2008;Erener and Düzgün, 2009]. Haralick features are the most common representation for spatial characteristics [Haralick et al., 1973]. Epifano and Soille [2007] emphasize the attributes of each type of texture in a colour image using 12 morphological transformations where each colour band (Red, Green and Blue) undergoes 4 transformations having the window size of 45 to 81 pixels. However, acquiring colour information may cause problems when same reflectance values correspond to very difficult textures [Patra and Mridula, 2011;Ma et al., 2015]. Hence, most of the methods [Constantinopoulos and Likas, 2007;Khalsa et al., 2010;Sadeghi et al., 2013;Mahajan et al., 2013; have used artificial texture datasets for testing a colour images, but these are not applicable for remote sensing natural images of forest, sea, and desert which have textures with high levels of natural variation. There have been several approaches and methods for texture segmentation proposed and discussed in earlier studies [Rao, 1990;Tuceryan and Jain, 1998;Wagner, 1999]. Cheng et al. [2013] proposed an improved rotation invariant uniform local binary pattern (RIU-LBP) operator for segmenting highresolution remote sensing images that have similar spectral information with diverse texture information. The error rate produced by the RIU-LBP is 14.21%. The statistical region merging (SRM) based segmentation algorithms proposed by Nock and Nielsen's [2004] produced 17.65 % error rate. The performance of the method proposed by Yuan et al. [2013] attains an overall accuracy of 64.7% which degrades when desiring a large segment number to deal with a composite scene and it is also more sensitive to noise. The extended LBP texture features using 3×3 window size with the lossless wavelet transform improves the segmentation results for image areas of water and woods but not in residential area [Guo et al., 2005]. A small window size will recognize finescale difference in pixel brightness while a large window may perhaps ignore significant variations in pixel [Kelsey and Neff, 2014]. The importance of a pixel or group of pixels in a segmented image depends on the nature of the application. Depending on the image resolution, the remote sensing image applications are classified into two: (1) high-resolution satellite image applications such as transportation network mapping, urban planning, precision farming and telecommunications and (2) low-resolution satellite image applications such as ecological estimation, provincial mapping, forestry supervision, widespread calamity assessment and urban monitoring [Cerra and Datcu, 2010;Singha and Hemachandran, 2011], where as the paper uses Landsat 8 Operational Land Imager (OLI) medium resolution images for forestry management using texture feature extraction method with segmentation algorithm. The Section 1 emphasizes the importance of texture feature extraction method which is employed in remote sensing images to identify the deforestation. Section 2 discusses the materials used in this study. The existing and proposed texture feature extractions and segmentation schemes are presented in Section 3. Section 4 describes the experiments and results used to investigate the research questions, and Section 5 presents the conclusions and future work.

Study area
The study area Sirumalai Hill, Dindigul district, Tamilnadu, India, is located between north 10° 07′ -10° 18′ and east 77° 55′ -78° 12′. It is a dense forest region with a moderate climate throughout the year. The temperature range varies from 29° C (84° F) to 37° C (99° F) in summer, 20° C (68° F) to 26° C (79° F) in winter and the annual average rainfall is 812 mm. The forest region of Sirumalai hill contains 178 medicinal plants and 57 medicinal and aroma plants. Deforestation and forest fire not only destroy the medicinal species in Sirumalai forest but also threaten the rich fauna and survival of ethnic groups living in the small pockets. Therefore, the determination of deforestation extent is an essential one to protect the rich medicinal species and ethnic groups. The study area is shown in Figure 1.

Remote sensing data
Bands from multispectral satellite representation are used to create colour composites that specifically make it easy for identification of the features of curiosity. Choosing the appropriate bands to be exploited in the colour image as supplementary has an enormous impact on which the features can be seen in the image. As shown in Table 1 (Band Details of Landsat 8), the band combinations of 4, 6 and 7 in Figure 2a is not able to separate the colour of forest regions from water bodies because both have more or less equal level of green component whereas the band combinations of 3, 5 and 7 can separate the colour of the forest from or water bodies. Hence, the proposed work uses the band combinations of 3, 5 and 7 as shown in Figure 2b. The Landsat 8 image resolutions are 15m, 30m and 100m for the panchromatic, multispectral and thermal bands. The accuracy of OLI and TIRS is 12 and 41 meters circular error that is 90% confidence. This study used dataset of Landsat 8 OLI images of Sirumalai hill which was acquired on 24 th May, 2013.

Modified texture feature extractions
Texture feature extraction method using the sum of difference in gray as well as colour image is found to be unsuitable for all types of remote sensing image applications to monitor forest fire, deforestation, water level calculations, etc., as it takes the average value of entire image pixels into consideration, which results in producing false segmentation accuracy of sensitive remote sensing applications like the analysis of deforestation as shown in Figure  1. Therefore, the paper introduces the Modified Sum of Difference (MSOD) using diagonal matrix and its combinations to find better accuracy of remote sensing images. The equation [1] and [3] are fundamental techniques for feature extraction.
where I represents the sample input image used in experiments, µ is the mean value of the image (I), g is the mean value of 3×3 window, n and m is total number of row and column values of I, i and j represents the row and column value of the image (I), and s, t are the 360º coordinates of 3×3 window. The variance proposed by Mahajan [9] using Haralick feature extraction is represented in equation n [1], and the MSOD for 3×3 windows is depicted in equation [3]. In equation [1], all the pixels in the image are subtracted from the mean value of the entire image pixels, but in an equation [3], each pixel in the image is subtracted from the mean value of neighbouring pixels, which is represented in equation [5].  Figure 3.

Figure -3 The process flow of the proposed method.
Depending on the application, the window matrix size may increase or decrease. In some remote sensing image applications, the larger window size may affect the accuracy of segmentation and feature extraction. The 3×3 window size in 30 meters image resolution may cover 270 sq. meters of forest area for averaging the pixel. If the image resolution is 30 meters the 5×5 window size covers 750 sq. meters, and its area coverage is high compared with 3×3 window size of 2.778% which is high in 5×5 window. However, the window size may increase subsequently as the area covered by window also increases. Therefore, the proposed work considers 3×3 window sizes for the feature extraction.

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After the texture feature extraction, a multiresolution algorithm is applied to the extracted texture feature to get smooth segmentation results. The result of multiresolution algorithm depends on the threshold value. A lower threshold value segments the image into a greater number of objects with a least mean difference; higher threshold values segment the image into lesser number of objects with the highest mean difference. Hence, the threshold value is chosen based on the application needs. According to separation of various densities of the forest for finding deforestation, the experiment carried out in the paper has chosen the threshold value of 200. The results and comparison of different texture feature extraction methods with multiresolution segmentation algorithm are discussed in the next section.

Results and discussion
Forest is a key element to a functioning and supporting an ecosystem service which includes bio-diversity, water yield and erosion control etc. Hence, keen observation and avoidance of deforestation and forest fire become an essential aspect of study to protect the ecosystem. Using manual methods to detect deforestation is a time-consuming process [Bruno et al., 2006;Asner et al., 2009;Gunlycke and Tuomaala, 2011;Arunarwati Margono et al., 2012]. Remote sensing image based segmentation, which is a less time-consuming process, helps in the management of forests and reduces vulnerability of deforestation and forest fire. The segmentation of deforested area and its accuracy are highly important to take precautionary actions, and thereby, reduce the cause of global warming problems. The Figure 4 and Table  2 depict that the proposed texture feature extraction gives a better result over the other texture feature derivations. The accuracy of the proposed work depends on three factors which includes texture feature extraction, threshold value of multiresolution algorithm and samples from Landsat 8 OLI image. To segment different objects such as various densities of forest, rocky region and vegetated region, etc, in the image, a threshold level of 200 is used in this study. To simplify the processing time of proposed work, 24 samples from the Landsat 8 OLI image have been taken into account for verifying the segmentation accuracy in this study. To verify the efficiency of the proposed work, the experiments are grouped into nine categories. The first algorithm is Haralick Sum of Difference which is used to derive the texture features like forest and rock; but it is failed in extricating the forest with vegetation area. The second algorithm is MSOD, which just produces the forest outline of the boundary besides creating the confusion between forests with non-forest cover. The next two algorithms are MSOA using Red with Blue Band and Green with Blue Band where the first one is used to extract precise rock regions and its performance which presents better results than Haralick Sum of Difference. The fourth algorithm is used to extract the urbanisation texture feature from the vegetation area but it does not separate the forest in rock province. The MSOD with MSOA is used to extract different textures available in the sample input image of the experiment such as forest, rock, urban and different level of nonforest regions as well. The accuracy of feature extraction in MSOD with MSOA is 93.26% and the remaining algorithms from 2-5 produce over-segmentation results of 137%, 119%, 127% and 140% respectively. The sixth and seventh algorithm are MSOD using left to right 3×3 diagonal matrix and right to left 3×3 diagonal matrix, where the first one is used for the finer segmentation of the rock with forest area and the second algorithm is used to segment different levels of forest and urban area available in the sample input image of the experiment. To get a better result, the eighth algorithm which is the combination of sixth and seventh algorithm separates all the objects available in the image with an average difference of 97.02% accuracy in texture feature extraction. Further, for the improvement of the accuracy, the 5×5 diagonal matrix is applied to the above methods which produce false alarm i.e. over-segmentation rate of -13%. These results are shown in Figure 4, 5 and Table 2. The following formulas are used to evaluate the segmentation accuracy and fault tolerance of proposed methods.  Table 3 depicts that the running time of the proposed feature extraction is much less compared to the other approaches.

Conclusions
Accurate surveying of deforestation is extremely significant for Forest Management Systems. For this reason, the paper proposes various texture feature extraction methods for segmenting the exact forest cover from a remote sensing image, which provides valuable information to the Forest and Fire Management Organisations to take precautionary actions.
In comparison with other approaches, the proposed method facilitates the improvement of the accuracy of remote sensing forest segmentation, as well as its characteristics including retrieving various objects such as densities (low, moderate and high) of forest and other components (rocky region, vegetation region, etc., ) as presented in the sample image of the experiments. The MSOD, using the combination of Left to Right and Right to Left 3×3 Diagonal Matrix, has been quite flexible and effective for the analysis of more complex forest model of Landsat 8 OLI images. Experimental results of this study point out that the segmentation accuracy has been improved to 97.02%. In future, further experimental investigations may be carried out in texture feature extraction to reach the maximum accuracy in remote sensing image segmentation.