pixelcooker

Aplikasi ArcGIS sebenarnya terdiri dari beberapa aplikasi dasar yaitu: ArcMap, ArcCatalog, ArcToolbox, ArcScene dan ArcGlobe. 1. ArcMap merupakan aplikasi utama yang digunakan untuk mengolah, membuat, menampilkan, memilih, editing dan layout peta. 2. ArcCatalog merupakan aplikasi yang berfungsi untuk mengatur berbagai macam data spasial dalam ArcMap, meliputi fungsi browsing, organizing, distributing, deleting data spasial. 3. ArcToolbox merupakan aplikasi perangkat/tools dalam melakukan analisis-analisis geospasial. 4. ArcScene merupakan aplikasi mengolah dan menampilkan peta-peta ke dalam bentuk 3D 5. ArcGlobe merupakan aplikasi yang berfungsi untuk menampilkan peta-peta 3D ke dalam bola dunia dan dapat dikoneksikan langsung dengan internet. ArcCatalog ArcCatalog adalah salah satu program dari ArcGIS yang bisa digunakan antara lain untuk menelusuri atau mencari data (browsing), mengorganisir (organizing), mendistribusikan (distributing) dan mendokumentasikan (documenting) suatu st

This is the newest satellite imagery cloud removal and correction tutorial I have made. The workflow described in this tutorial is similar to my previous tutorial about satellite imagery clouds removal and correction using ArcGIS Desktop. The only difference is, the workflow I described in this video is better. Because this time, we could use the QA Band as the reference cloud mask. So, it is more simple, robust, accurate, precise, and guarantees a better result. It is all thanks to Graphical Raster Functions Editor implemented in ArcGIS Pro. 

Satellite Imagery Orthorectification usually uses interior/exterior orientation information for the photogrammetric collinear equation to works. Satellite Imagery vendors usually gave this information to the users in a form of a sequence of constant numbers written in a certain format and can be consumed by photogrammetry software. This information is called Rational Polynomial Coefficients (RPC) and is usually provided along with other imagery metadata by satellite imagery companies.  This RPC information became the backbone of orthorectification. Without this RPC information, orthorectification can't be done and can't be performed. Fortunately, some remote sensing software can compute this RPC information from the Sensor Characteristics information and it can also be more accurate if complemented by Ground Control Points measured in the field or measured from more accurate imagery.  I will explain how to do this later. In this article, I will show you how to perform orthorect

Numerous Pan-sharpening Algorithm has been developed. Some of them are better in preserve the spatial details of the panchromatic band but have poor spectral information preservation of multispectral bands, and vice versa. ArcGIS has implemented some of the most well-known Pan-Sharpening algorithms into its roster of geospatial tools. Some are good, some are good only in certain aspects.  Fortunately, Pan Sharpening implementation in ArcGIS also complemented with a weighting factor. These weights value can be derived from the data-driven approach. By using these weights, we can further control the Pan-Sharpening algorithm that has poor ability to maintain spectral information like IHS to be more consistent with the multispectral bands. And this is what the video tutorial below is about. 

Satellite Imagery Orthorectification usually uses interior/exterior orientation information for the photogrammetric collinear equation to works. Satellite Imagery vendors usually gave this information to the users in a form of a sequence of constant numbers written in a certain format and can be consumed by photogrammetry software. This information is called Rational Polynomial Coefficients (RPC) and usually provided along with other imagery metadata by satellite imagery companies.  This RPC information became the backbone of orthorectification. Without this RPC information, orthorectification can't be done and can't be performed. Fortunately, some of the remote sensing software has capabilities to compute this RPC information from the Sensor Characteristics information and complemented by Ground Control Points measured in the field or more accurate imagery.  I will explain how to do this later. In this article, I will show you how to perform orthorectification using custom or

When building RPCs for digital camera aerial photography and pushbroom sensor imagery, you will need to enter various required parameters such as   principal points ,   focal lengths and pixel sizes , and   incidence angles . This section provides guidelines on determining these values. Principal Point Coordinates Principal point coordinates are often set to [0.0, 0.0], which assumes that the principal point is the center of the image for a frame central projection and the center of each scan line for a line central projection. A laboratory calibration report should provide the principal point coordinates. Focal Length and Pixel Size Focal length is the orthogonal distance from the perspective center to the image focal plane. Pixel sizes correspond to the CCD cells (detectors of the camera that captured the images). Typically, aerial digital cameras and satellite pushbroom sensors have square pixels, which means that the pixel size is the same in the x and y dimensions. Focal length an

Cloud cover is a long-time problem for optical imagery analysis in remote sensing. It is obscuring important earth surface object that became the main target on image analysis. There are numerous methods and efforts have been developed to fix the cloud cover problem. The solutions are coming from simple approaches like masking and replacing using a raster calculator or map algebra, to more complex ways like multitemporal pixels blending, fast Fourier transform-based filtering, or even machine learning methods.  The correction results also vary. Sometimes certain method could give a nearly perfect cloudless image, but sometimes it doesn't. One simple way to remove cloud in optical imagery is using the masking and replacing method. This method needs at least two imagery covering the same area at different dates, so it is expected that two imagery will have different cloud cover conditions, and finally, both of them can be merged together to create a cloudless image.  This approach ha