Automatic information extraction requires a processing system to encapsulate the content of the image. This is a non-trivial task, because of the complexity of the information stored in images. In this paper satellite image enhancement and smoothing towards automatic feature extraction is accomplished through an effective serial application of anisotropic diffusion processing and alternating sequential filtering. Nonlinear diffusion processes can be found in many recent methods for image processing and computer vision. A robust anisotropic diffusion filtering is used with Tukey's biweight robust error norm for "edge-stopping" function, which preserves sharper boundaries than previous formulations and improves the automatic stopping of the diffusion. A well-known class of morphological filters, alternating sequential filtering is applied afterwards for a more extended enhancement and smoothing. The effective processing scheme is demonstrated with examples; Results appear promising.
This paper describes the aim towards a nonlinear processing system for satellite image enhancement and smoothing that prepares image for a successful feature extraction through edge detection. Emphasis was given to coastlines, man made objects such as airports, dams, buildings and linear features such as roads and parcel boundaries. Rank order morphological operators and adaptive filtering were employed leading to a promising result. Adaptive filtering was adopted to smooth the image, homogenize regions and at the same time prohibit edge blurring, since high frequency areas in the image are protected. Morphological operators based on rank filters were also implemented because they are often more robust to noise and shape variations than morphological operators with plain structuring element. A survey concerning the shape and the size for the structuring element used for the morphological operators is presented. Structuring element’s shape can lead to certain transformation of desired features geometry and size controls the scale space, trying to retain only features at certain desirable scales. The nonlinear processing system was applied to SPOT HRV (10-meters ground resolution) and IKONOS PAN (1-meter ground resolution) satellite imagery and is demonstrated with examples.
This paper describes the experience gained from the evaluation of selected automatic edge detection techniques applied to LANDSAT TM, SPOT HRV, IRS 1C and IKONOS images. Emphasis was given to the detection of man-made objects and linear features such as coastlines, roads and parcel boundaries in combination with selected preprocessing and postprocessing operations. As preprocessing Gaussian, adaptive and morphological operators were implemented and tested for image enhancement and smoothing. Edge extraction processing followed. First the Canny edge detector was applied. Then a morphological nonlinear Laplacian operator was applied and its zero-crossings yielded edge locations. Finally an edge detector resulting by overlaying two thresholded images from the Prewitt gradient, preserving edges appearing in both images, was applied. Postprocessing followed to eliminate noisy edges and restore edge connectivity through morphological operators. An analysis of the relative performance of the processing scheme indicated each detector's relation to noise (features at certain undesired scales, shadows along roads boundaries, irrelevant edges within parcel boundaries) and the set of specific parameters needed for proper enhancement and smoothing before edge extraction.