Glacier mapping with object based image analysis method, case study of Mount Everest region

Substantial progress in Geoinformatics System in recent years leads to the research in monitoring and mapping of glaciers. Monitoring glacier in the mountain region with traditional manual method is very crucial and time-consuming. Glaciers are melting because of global warming. Melting of glaciers can causes calamities like rising in sea level, glacial lake outburst, avalanches etc. Glacier monitoring using multi-temporal data for objects on the surface of the glacier is hard to classify. This paper gives an insight into the importance of Geo-spatial data and object-based image analysis method for satellite image processing. The object-based image analysis benefits more compared to traditional pixel-based image analysis as it is more robust and noise removing more image features etc. Spectral data with multiple bands is the backbone of surveying and monitoring of glacier. In this paper case study of Mount Everest region (27 48° 32N, 86 54° 47E) is represented. The remotely sensed image needs to be taken in a cloud-free environment. Object-based image classification is done in recognition tool. Also, the step by step methodology of object-based classification, segmentation and post-classification possibilities are discussed. Finally, the paper presents several representations of indexes. The integration of indexes is useful for accurately classifying the different part of terrain, lake, vegetation and glacier

Object segmentation in still images using topic modelling method

One of the key components towards achieving high performance automated visual-based object recognition is the quasi-error free object segmentation process. Being an important integral part of many machine vision as well as computer vision systems, a tremendous amount of effort in object segmentation has been proposed in the literature. One of these approaches is the work that implements Probabilistic Graph Modelling (PGM) techniques. PGM is a rich framework for calculating probability and statistics in large given data sets and fields. One of the comprehensive methods in PGM is the Topic Modelling (TM) method introduced in the early 2000. TM has shown to be successful in classifying humongous information related to text and documents and has been implemented in many online search engines. Since image contains huge amount of information (in terms of pixels), segmentation of this information into meaningful region of interest (in this case objects) does fit into the framework of TM. The objectives of this project are to implement and analyze the capability and efficiency of TM in recognizing objects found in stationary images. TM is a process where it uses approximation technique to discover important segment or structure based on object classification. However, to proceed with object classification, object segmentation is firstly executed, making object segmentation as the most important part in the system. Through TM, the classification can be done by grouping the pixels (superpixels) accordingly in order to clearly represent the object of interest. In achieving this goals, Open Computer Vision (OpenCV) library will be fully utilized. It is expected that the proposed method will be able to perform object segmentation with high confident similar to state-of-the-art methods

Distance to Center of Mass Encoding for Instance Segmentation

The instance segmentation can be considered an extension of the object detection problem where bounding boxes are replaced by object contours. Strictly speaking the problem requires to identify each pixel instance and class independently of the artifice used for this mean. The advantage of instance segmentation over the usual object detection lies in the precise delineation of objects improving object localization. Additionally, object contours allow the evaluation of partial occlusion with basic image processing algorithms. This work approaches the instance segmentation problem as an annotation problem and presents a novel technique to encode and decode ground truth annotations. We propose a mathematical representation of instances that any deep semantic segmentation model can learn and generalize. Each individual instance is represented by a center of mass and a field of vectors pointing to it. This encoding technique has been denominated Distance to Center of Mass Encoding (DCME)