A processor-implemented method for visual scene change detection while performing visual inspection of an area using an Unmanned Vehicle, UV, comprising: capturing at least one image of a target, via one or more hardware processors, by an image capturing module of the UV; identifying a reference image that matches the captured at least one image, from a plurality of reference images, via the one or more hardware processors, by the image processing module of the UV; generating a similarity map based on the captured at least one image, I test and the reference image,I ref, via the one or more hardware processors, wherein the similarity map highlights changes between the captured at least one image and the reference image, wherein the similarity map is generated based on pixel based similarity and structural similarity, wherein generating the similarity map comprises: computing structural similarity values between the captured at least one image with the identified reference image at pixel level; and generating the similarity map, I SSIM, based on the computed structural similarity values; and characterised by: extracting differences between the captured at least one image and the reference image, by computing mean similarity within each super pixel by performing a multi-scale super pixel analysis of the similarity map, I SSIM,, via the one or more hardware processors, wherein performing the multi-scale super pixel analysis of the similarity map comprises: computing a Gaussian pyramid of the captured at least one image and corresponding reference image and the corresponding similarity map, I SSIM, at a plurality of K different scales; generating multi-scale super pixels at different scales, S k, for the at least one captured image, at each of the plurality of K different scales, wherein the multi-scale super-pixels are generated by clustering nearby similar pixels; generating a super-pixel based change map, by computing mean structural similarity within each multi-scale super-pixel S k using equation I CM (S k) = mean (I SSIM (S k)) and combining corresponding multi-scale super-pixels, by up-scaling and adding from each of the plurality of K different scales of the captured at least one image, to obtain a set of N super-pixel based change maps, wherein each super pixel based change map is assigned with a weight factor; generating a combined change map, based on the set of N super-pixel based change maps by multiplying each of the super-pixel based change maps with a corresponding weight factor, and adding them using equation I CCM =ω1I 1 CM +ω 2 I 2 CM +.....+ω N I N CM where I CCM is the combined change map, I N CM and ω N are the N th change map and N th weight factor respectively; and identifying overlapping regions between the combined change map and the reference image. capturing at least one image of a target, via one or more hardware processors, by an image capturing module of the UV; identifying a reference image that matches the captured at least one image, from a plurality of reference images, via the one or more hardware processors, by the image processing module of the UV; generating a similarity map based on the captured at least one image, I test and the reference image,I ref, via the one or more hardware processors, wherein the similarity map highlights changes between the captured at least one image and the reference image, wherein the similarity map is generated based on pixel based similarity and structural similarity, wherein generating the similarity map comprises: computing structural similarity values between the captured at least one image with the identified reference image at pixel level; and generating the similarity map, I SSIM, based on the computed structural similarity values; and computing structural similarity values between the captured at least one image with the identified reference image at pixel level; and generating the similarity map, I SSIM, based on the computed structural similarity values; and computing a Gaussian pyramid of the captured at least one image and corresponding reference image and the corresponding similarity map, I SSIM, at a plurality of K different scales; generating multi-scale super pixels at different scales, S k, for the at least one captured image, at each of the plurality of K different scales, wherein the multi-scale super-pixels are generated by clustering nearby similar pixels; generating a super-pixel based change map, by computing mean structural similarity within each multi-scale super-pixel S k using equation I CM (S k) = mean (I SSIM (S k)) and combining corresponding multi-scale super-pixels, by up-scaling and adding from each of the plurality of K different scales of the captured at least one image, to obtain a set of N super-pixel based change maps, wherein each super pixel based change map is assigned with a weight factor; generating a combined change map, based on the set of N super-pixel based change maps by multiplying each of the super-pixel based change maps with a corresponding weight factor, and adding them using equation I CCM =ω1I 1 CM +ω 2 I 2 CM +.....+ω N I N CM where I CCM is the combined change map, I N CM and ω N are the N th change map and N th weight factor respectively; and identifying overlapping regions between the combined change map and the reference image.