In this paper, a texture-based segmentation approach using wavelet packets, co-occurrence matrices and normalised modified histogram thresholding is discussed and developed. Background and objects in remotely sensed light detection and ranging (LIDAR) data are successfully partitioned into rivers, fields and residential areas using the developed algorithms. The issue of wavelet packet decomposition level is addressed by analysing the sub-images' energy and entropy. The standard deviation of the modified histogram, which is derived from the main diagonal of the sub-image's co-occurrence matrix, is used as a measure to evaluate the sub-images in terms of thresholdability. Finally, the segmentation results are presented.