A processor implemented method for classification and segmentation of high-resolution images, the method comprising: receiving, via one or more hardware processors, a high-resolution image; dividing, by a neuromorphic platform, the high-resolution image into a plurality of first size patches, wherein a patch-size at each level of a hierarchical multi-stage Spiking Neural Network classifier, and a plurality of percentage threshold values defining a) a clean patch, b) a partially covered patch, and c) a completely covered patch at each level of the hierarchical multi-stage SNN classifier are determined based on a size-performance criteria derived using a tuple of five metrics comprising: a) threshold pixel coverage, b) threshold area coverage, c) pixelwise accuracy, d) classwise-Intersection over Union (IoU), and e) Mean-IoU, and wherein the patch-size decreases gradually from a first SNN classifier to a last SNN classifier of a plurality of SNN classifiers of the hierarchical multi-stage SNN classifier; performing, by the neuromorphic platform, a multi-class classification of each patch of the plurality of first size patches based on the plurality of threshold values determined for the first SNN classifier of the neuromorphic platform, from among the plurality of SNN classifiers, wherein each patch is assigned a class label by the first SNN classifier for a classification objective, wherein the class labels comprise one of a) the clean patch b) the partially covered patch and c) the completely covered patch; identifying, by the neuromorphic platform, a partial image of the high-resolution image that corresponds to patches labelled as partially covered patch; dividing, by the neuromorphic platform, the identified partial image into a plurality of second size patches; performing, by the neuromorphic platform, a binary classification of each of the plurality of second size patches if the second SNN classifier is the last SNN classifier, wherein the binary classification is based on the plurality of threshold values determined for a second SNN classifier from among the plurality of classifiers, wherein the class labels comprise a) the clean patch, and b) the completely covered patch; generating, by the neuromorphic platform, a patch level segmentation map of the high-resolution image by aggregating class labels at each level; computing, by the one or more hardware processors, an overall percentage cover for the high-resolution image by analyzing area covered by the patches labelled as clean patches based on the class labels at each level; and marking, by the one or more hardware processors, the high-resolution image as a highly covered image if the overall percentage cover of the high-resolution image is above a predefined cover threshold. receiving, via one or more hardware processors, a high-resolution image; dividing, by a neuromorphic platform, the high-resolution image into a plurality of first size patches, wherein a patch-size at each level of a hierarchical multi-stage Spiking Neural Network classifier, and a plurality of percentage threshold values defining a) a clean patch, b) a partially covered patch, and c) a completely covered patch at each level of the hierarchical multi-stage SNN classifier are determined based on a size-performance criteria derived using a tuple of five metrics comprising: a) threshold pixel coverage, b) threshold area coverage, c) pixelwise accuracy, d) classwise-Intersection over Union (IoU), and e) Mean-IoU, and wherein the patch-size decreases gradually from a first SNN classifier to a last SNN classifier of a plurality of SNN classifiers of the hierarchical multi-stage SNN classifier; performing, by the neuromorphic platform, a multi-class classification of each patch of the plurality of first size patches based on the plurality of threshold values determined for the first SNN classifier of the neuromorphic platform, from among the plurality of SNN classifiers, wherein each patch is assigned a class label by the first SNN classifier for a classification objective, wherein the class labels comprise one of a) the clean patch b) the partially covered patch and c) the completely covered patch; identifying, by the neuromorphic platform, a partial image of the high-resolution image that corresponds to patches labelled as partially covered patch; dividing, by the neuromorphic platform, the identified partial image into a plurality of second size patches; performing, by the neuromorphic platform, a binary classification of each of the plurality of second size patches if the second SNN classifier is the last SNN classifier, wherein the binary classification is based on the plurality of threshold values determined for a second SNN classifier from among the plurality of classifiers, wherein the class labels comprise a) the clean patch, and b) the completely covered patch; generating, by the neuromorphic platform, a patch level segmentation map of the high-resolution image by aggregating class labels at each level; computing, by the one or more hardware processors, an overall percentage cover for the high-resolution image by analyzing area covered by the patches labelled as clean patches based on the class labels at each level; and marking, by the one or more hardware processors, the high-resolution image as a highly covered image if the overall percentage cover of the high-resolution image is above a predefined cover threshold.