An Adaptive Thresholding Approach Based On Improved Harris Corner Detection For Estimation Of Built Up Region From Remote Sensing Images
This paper proposes an approach to estimate the possible built-up areas from high-resolution remote sensing images covering different scenes for monitoring the built- up areas within limited time and minimal cost. The motivation behind this work is that the frequently recurring patterns or repeated textures corresponding to common objects of interest (e.g., built-up areas) in the input image data can help in discriminating the built-up areas from others. The proposed method consists of two steps. First step involves extracting a large set of corners from the input image by employing an improved Harris Corner detector. The improved Harris Corner selects the local maxima from the extracted corners by performing the gray scale morphological dilation operation. It then finds those points in the corner strength image that matches the dilated image and is greater than the threshold value. In the second step, an adaptive global thresholding is applied to the corner response image and binary morphological operations are performed to obtain the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in the literature in terms of detection accuracy.Keywords: Harris corner, spectrum clustering, global thresholdingI.
Introduction
Remote sensing technologies play a vital role in sourcing the information in fields like geography, surveillance, city planning etc., It is instrumental in gauging the distribution, growth and characteristics of built up area and it can be of great help in updating the land maps and draw city Plans. One of the more important steps in Remote sensing Technologies is extraction of the built up Regions/ Urbanized regions from the satellite images. Most current techniques that are used to find urbanized areas are based on texture analysis. The built up area represents both manmade and Natural objects since the texture of the scene is often not clear from that of the natural objects.While accessing the urban information, it is often seen that there are repeated patterns or textures, which makes it difficult indiscriminating the built-up area and the natural objects, hence an unsubstantiated approach was proposed to detect built up regions using high-resolution satellite images. It is often observed that urban areas exhibit a multitude of straight line features. The numerical measurement of these straight lines helps in analyzing the measurement of length, orientation and locality.
A new segmentation method used on the connected section in images based on syntactic characteristics was proposed which make use of the semantic leveling and range for defining the characteristics. This may result in producing an elaborate effect, a border effect and vagueness in discriminating the object from the background.
The mathematical semantic methods are used for the high phantom of data to classify the shadowy data with high altitudinal resolution to investigate the urban areas. This approach helps in bringing the transformations in isolation of the bright and dark structures in the images which mean that the images are brighter or darker than the adjacent features in the images. The two high semantic urban datasets are further classified. This method is further used for visual network and are compared with other methods of numerical components and feature extractions. The high resolution satellite images provide vital information to the remote sensing devices in monitoring the urban regions. Hence these high resolution areal satellite images helps in optimum results and was tested on various three dimensional aerials and Satellite image data set.
The challenges of detecting the built-up area images is hence proposed through high resolution satellite images under the perception that the built up areas often detected through these images and can be measured by the geometrical structures. This method proposes a systematic beginning through this approach for gauging the built up area estimation.This work proposes a use of adaptive global thresholding approach for built-up area estimation. The remainder of this article Is segmented into different sections: SectionII discusses about the proposed method and its step by step implementation process, Section III provides the discussion about the results obtained and section IV provides the conclusion about the present work and the scope for future enhancements.
Proposed method
This work proposes a framework towards discovering built- up regions from high-resolution satellite images. The proposed method includes two major components:
- A likelihood function based approach to extract candidate built-up regions in which an improved Harris corner detection operation is proposed.
- Adaptive global Thresholding based algorithm for the Built-up area detection.
Results and discussion
The proposed method has been evaluated on the dataset consisting of five high resolution remote sensing images with varied spatial resolution. The performance parameter considered is the number of segments and the area of segments. The performance comparison of the proposed method is done with the Otsu thresholding and Graph cut method. In order to compare the system performance, built-up regions in the original image are marked manually and are compared against the system output. The results of the proposed method, Otsu thresholding and Graph cut method are indicated for few randomly selected images from the dataset. Fig 8, shows the randomly selected original image from the dataset, Fig.9 shows the manually marked built-up areas, Fig.10, Fig.11 and Fig.12 depicts the output of proposed, Graph cut and Otsu method respectively. Further Fig.13, Fig.14 and Fig.15 indicates that the difference between the manually marked images and the output obtained from the adaptive threshold method on the corresponding panchromatic satellite image is negligible. Table.1 compares the segmentation of proposed method with competing methods, such as graph cut and OTSU method. First row against each method in theTable.1indicates the number of segments and the second row indicates pixel count corresponding to built-up region. It is observed from the resultant images and from Table.1 that the proposed method is considerably accurate, and is able to segment built-up areas well. In doing the comparison, a quantitative measure of the number of segments deciphered by the newly proposed method and manual method has been used. It is observed that the proposed method results are visually more accurate. While the proposed method is robust in most scenarios, it produces false positives, in cases where there are prominent corner features in natural habitat, such as the “forest road” areas as depicted in Fig7.
Conclusion
This work present a framework for estimating the built-up regions from satellite images. The proposed method includes two major components, first being a likelihood-function to extract candidate built-up regions, in which an improved Harris operation is proposed, and the second being adaptive Global-Thresholding based algorithm for the final built-up area detection. Based on the results obtained, the proposed approach demonstrates increased accuracy when compared to the existing works in the literature. Future work in this direction is to improve the detection using machine-learning classification models, which would not only allow segmenting the urban areas in a much cleaner way, but also allow reading the city texture in a much detailed way.
