Detecting informal settlements has become an important area of research in the past decade, owing to the availability of high resolution satellite imagery. Traditional per-pixel based classification methods provide high degree of accuracy in distinguishing primitive instances such as buildings, roads, forests and water. However, these methods fail to capture the complex relationships between neighboring pixels that is necessary for distinguishing complex objects such as informal and formal settlements. In this paper, we perform several experiments to compare and contrast how various per-pixel based classification methods, when combined with various features perform in detecting slums. In addition, we also explored a deep neural network, which showed better accuracy than the pixel based methods.