Classification of Fallow and Perennial Fields in High-Resolution Multispectral Aerial Images - Akhoury, Sharat Saurabh (Department of Electrical Engineering, 2021)
Increased cultivation of perennial fields hardens the water demand by the agricultural sector during drought events. It is therefore important to detect and track these fields to better plan for drought mitigation and response strategies. Remote sensing offers an effective means by which this can be accomplished. An interesting and challenging problem is presented in some cases of remotely sensed perennial fields which are readily confused with ill-maintained, abandoned and weed-infested fallow fields. The spectral response of such cases are highly correlated, hence conventional remote sensing indicators fail to discriminate between these two terrains. The work undertaken in this research attempts to address this problem by applying machine learning-based solutions for providing accurate and scalable valuations of perennial and fallow fields using high resolution multi-spectral remote sensing data. The distinctive uniform grid-like representation of perennial acreage motivated the use of a texture-based classification approach. Two different texture classification methods are developed, namely a pixel-based image analysis texture segmentation framework (TSF) approach and a statistical vocabulary learning-based approach referred to as the VarmaZisserman classifier (VZC). In the first approach, the texture classification problem is reformulated as a texture segmentation problem in which each pixel in the image is individually labelled by training a classifier on the texture feature space. In the second approach, a set of images is used to generate a texton dictionary from which exemplar texture probabilistic models are learnt. Three transform-based techniques are applied for computing texture features. Experimental results validate that a texture-based machine learning approach is able to successfully discriminate between fallow and perennial land cover with an error rate ranging between 6.6% (TSF) and 16.8% (VZC). The pixel-based image analysis approach is found to be more conducive for classifying homogenous land cover types that have high interclass spectral reflectance overlap. A comprehensive multi-classifier experiment indicates that ensemble-based classifiers (such as random forests and AdaBoost) and instance-based classifiers (such as k-nearest neighbours) are better suited at identifying agricultural land covers with correlated spectral responses. These classifiers yield precision and recall scores ≥ 90% with error rates less than 10%. It is shown that a deterministic sampling technique such as striding can greatly reduce the learning rate as well as model size without compromising the classification accuracy, precision and recall.